Datasets:
name stringlengths 3 24 | description stringlengths 438 1.15k | requirements listlengths 6 16 | interface unknown | domain stringclasses 11
values | era dict | difficulty stringclasses 3
values | est_loc int64 150 4.5k | forbidden listlengths 1 8 | tags listlengths 3 7 | provenance dict | id stringlengths 15 15 | pipeline dict | verification unknown |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bori-dam-analysis | This program performs hydraulic post-processing and engineering visualization for dam safety analysis, specifically targeting the Kurnur (BORI) Dam. It processes raw time-series data exported from HEC-RAS simulations to evaluate three distinct dam failure scenarios: piping failure, overtopping failure, and controlled r... | [
"Parses input Excel files containing time-series data for headwater elevation, tailwater elevation, and total discharge",
"Identifies and records the peak discharge value and the specific timestamp at which it occurs for each hydrograph in stdout summary",
"Calculates the drawdown slope of the reservoir during ... | {
"invocation": "cli",
"argv": "bori-dam-analysis --input-dir path/ --output-dir path/",
"stdin": "none",
"stdout": "status updates and summary of statistics",
"stderr": "error messages regarding file parsing or checksum mismatches",
"exit_codes": {
"0": "analysis and visualization completed successfull... | numerical | {
"year": 2026,
"platform": "cross-platform",
"language": "Python 3.10",
"constraints": [
"requires openpyxl for excel parsing",
"requires sufficient memory for large time-series dataframes"
]
} | medium | 800 | [
"HEC-RAS simulation engine",
"black-box hydraulic solvers",
"automated data smoothing libraries"
] | [
"hydrology",
"dam-safety",
"hydrograph",
"visualization"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "007-Wik__Dam-Hydrographs-Analysis-BORI",
"archive_path": "github/github/007-Wik__Dam-Hydrographs-Analysis-BORI",
"posting_year": 2026
} | ri-5bfe8cf480a0 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
poisson-kac | This program simulates the Poisson-Kac process, a one-dimensional random walk characterized by a finite propagation velocity and random direction reversals. Unlike classical diffusion models that assume infinite signal speed, this model uses hyperbolic dynamics to capture realistic relaxation phenomena in physical syst... | [
"Generate N random velocity reversal times using inverse transform sampling tau = -ln(1-P)/mu and verify the mean time between events is 1/mu for large N.",
"Construct a continuous particle trajectory x(t) starting at x(0) with velocity s, reversing direction at each tau event, and output the position at specific... | {
"invocation": "cli",
"argv": "poisson-kac --mode <continuous|discrete> --mu <rate> --s <speed> --L <length> --x0 <start> --dt <step> --steps <count>",
"stdin": "none",
"stdout": "Statistical summaries including exit probabilities and mean residence times.",
"stderr": "Simulation progress updates and error m... | numerical | {
"year": 2026,
"platform": "specified",
"language": "Python",
"constraints": [
"requires a high-quality random number generator",
"floating point precision required for exponential decay calculations"
]
} | medium | 850 | [
"numpy",
"scipy",
"matplotlib",
"pandas"
] | [
"simulation",
"stochastic",
"physics",
"diffusion"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "04milano__VibeCoders-Poisson-Kac",
"archive_path": "github/github/04milano__VibeCoders-Poisson-Kac",
"posting_year": 2026
} | ri-13f8eafca3a2 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 1,
"observed": 1,
"strong": 0,
"attempts": 2
} |
sod-shocktube-cfd | This program is a one-dimensional computational fluid dynamics solver designed to simulate Euler equations for compressible inviscid flow using the Sod shock tube problem. The simulation models a scenario where a high-pressure gas expands into a low-pressure gas region, generating a shock wave, a rarefaction wave, and ... | [
"Initialize a uniform mesh from x=x_left to x=x_right with a specified number of points n_points (minimum 10).",
"Assign initial density, velocity, and pressure to the left and right states of a diaphragm at diaphragm_pos.",
"Implement exactly nine numerical flux schemes: Lax-Friedrichs, Lax-Wendroff, MacCormac... | {
"invocation": "cli",
"argv": "python run_simulation.py [--n_points N] [--schemes S1 S2] [--bc TYPE] [--left_rho VAL] [--left_p VAL] [--right_rho VAL] [--right_p VAL] [--config FILE] [--config-json FILE]",
"stdin": "none",
"stdout": "Simulation progress and final error statistics",
"stderr": "Error messages ... | numerical | {
"year": 2026,
"platform": "generic",
"language": "Python 3.9+",
"constraints": [
"memory limits for high-resolution mesh arrays",
"requires external libraries for YAML parsing if not used"
]
} | medium | 1,200 | [
"scipy.integrate",
"openfoam",
"any-black-box-riemann-solverlibrary"
] | [
"cfd",
"numerical-methods",
"sod-tube",
"euler-equations"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "050610lzl__sod-shocktube-cfd",
"archive_path": "github/github/050610lzl__sod-shocktube-cfd",
"posting_year": 2026
} | ri-d340b8383dbc | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "error: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbf in position 5: invalid start byte"
} |
quiet-lab | A sophisticated adaptive fan control system designed for the Supermicro ARS-210M-NR server. Unlike standard datacenter fan curves that prioritize worst-case thermal scenarios—resulting in excessive noise for homelabs—this tool uses Model Predictive Control (MPC) and an Extended Kalman Filter (EKF) to learn the specific... | [
"Initialize all supported system fans to 100% PWM immediately upon startup to ensure safety before control begins",
"Interface with hardware via ipmitool to read current temperatures from hardcoded thermal nodes and set fan PWM",
"Print the EKF thermal state vector and heat transfer coefficients to stdout to ve... | {
"invocation": "cli",
"argv": "quiet-lab [--config-path] [--mode production|learning]",
"stdin": "none",
"stdout": "current status including temperatures, fan speeds, and convergence metrics",
"stderr": "errors regarding IPMI communication or thermal violations",
"exit_codes": {
"0": "normal operation"... | systems | {
"year": 2025,
"platform": "Linux",
"language": "Python 3.8+",
"constraints": [
"requires root access for IPMI",
"requires ipmitool installed on system",
"requires numpy and scipy for matrix operations"
]
} | hard | 1,800 | [
"black-box MPC solvers",
"existing CFD thermal frameworks",
"supermicro-specific fan control libraries"
] | [
"thermal-management",
"fan-control",
"ipmi",
"mpc",
"kalman-filter"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "0xAlcibiades__quiet-lab",
"archive_path": "github/github/0xAlcibiades__quiet-lab",
"posting_year": 2025
} | ri-434840584b4d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 3,
"observed": 3,
"strong": 3,
"attempts": 3
} |
hvaf-plume-analysis | This tool performs qualitative diagnostics on Activated-Combustion High-Velocity Air-Fuel (AC-HVAF) spray plumes using experimental data. The program processes 1D axial intensity profiles derived from video frames, representing the plume across multiple time steps. The primary objective is to determine the flame length... | [
"Reads a CSV file containing an 'x_mm' column and multiple 'luma_#####' columns representing frame intensity profiles",
"Identifies the maximum luma value for each frame column in the dataset",
"Calculates a detection threshold for each frame by multiplying the peak intensity by the user-defined fraction (thres... | {
"invocation": "cli",
"argv": "hvaf-plume-analysis <input.csv> --threshold <value>",
"stdin": "none",
"stdout": "frame_index, flame_length_mm",
"stderr": "error messages and usage instructions",
"exit_codes": {
"0": "analysis completed successfully",
"1": "file format error",
"2": "invalid argu... | numerical | {
"year": 2025,
"platform": "generic",
"language": "Python 3.12",
"constraints": [
"memory-limited by large multi-frame CSV datasets",
"requires floating point precision for coordinate mapping"
]
} | medium | 600 | [
"opencv",
"scipy.signal",
"image"
] | [
"cfd",
"plume-dynamics",
"signal-processing",
"csv-analysis"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "102victor__Computational-Analysis-of-AC-HVAF-Spray-Plume-Diagnostics",
"archive_path": "github/github/102victor__Computational-Analysis-of-AC-HVAF-Spray-Plume-Diagnostics",
"posti... | ri-e2c3fccc08a4 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
differential-evolution | This program implements a high-fidelity Differential Evolution (DE) framework for global optimization of complex engineering problems. Differential Evolution is a stochastic search algorithm that evolves a population of candidate solutions through mutation, crossover, and selection. This specific implementation is desi... | [
"Provide a solver function that accepts an objective function, lower bounds, upper bounds, population size, and maximum iteration count as input.",
"Initialize a population of size N uniformly distributed between the provided lower and upper bounds vectors.",
"Perform selection such that the trial vector replac... | {
"invocation": "library",
"argv": "",
"stdin": "none",
"stdout": "The function returns a struct or object containing optimization results to the caller.",
"stderr": "error messages or warnings during execution.",
"exit_codes": {
"0": "successful optimization",
"1": "invalid input parameters or conv... | numerical | {
"year": 2024,
"platform": "generic",
"language": "Julia 1.10+",
"constraints": [
"requires multi-threading support via Threads.@spawn",
"memory availability for large population histories"
]
} | medium | 1,200 | [
"DifferentialEvolution.jl",
"GlobalOptim.jl",
"black-box DE solvers"
] | [
"optimization",
"differential-evolution",
"parallel",
"numerical"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "1055I3__differential_evolution",
"archive_path": "github/github/1055I3__differential_evolution",
"posting_year": 2024
} | ri-6b945ff5f53e | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
cement-pred | This machine learning system is designed to predict the compressive strength of concrete based on its chemical composition and curing age. The system handles the end-to-end data lifecycle: from rigorous validation against a provided schema to complex cluster-based modeling. Because concrete mixtures vary significantly ... | [
"Accept a JSON schema file defining expected filename patterns, date/time lengths, column counts, and data types",
"Validate input filenames against the schema regex and length requirements, moving invalid files to a 'Bad_Data_Folder'",
"Move CSV files to 'Bad_Data_Folder' if the column count or column names do... | {
"invocation": "cli",
"argv": "cement-pred <schema.json> <input_dir> <db_name> <output_path>",
"stdin": "none",
"stdout": "Path to the generated prediction CSV file and status logs",
"stderr": "Validation errors and database connection warnings",
"exit_codes": {
"0": "Execution completed successfully",... | ai | {
"year": 2022,
"platform": "Cloud (Heroku)",
"language": "Python",
"constraints": [
"requires sufficient memory for KNN imputation on large datasets",
"requires database driver for persistent storage"
]
} | hard | 2,200 | [
"scikit-learn",
"pandas",
"sqlalchemy",
"flask"
] | [
"machine-learning",
"regression",
"concrete-science",
"data-pipeline",
"clustering"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "1224harsh__Cement-Strength-prediction-ML",
"archive_path": "github/github/1224harsh__Cement-Strength-prediction-ML",
"posting_year": 2022
} | ri-9e26d73ac24f | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
hydro-blade-analysis | This program provides a pipeline for the multi-objective evaluation of micro-turbine blades specifically designed for hydrogen combustion. Hydrogen environments present unique thermal and structural challenges, requiring precise parametric analysis of blade geometries. The tool bridges the gap between raw 3D geometric ... | [
"Accepts an input STL file via the --input flag and an output directory via the --output flag",
"Parses the provided STL file to identify blade geometry and generate spanwise stations",
"Generates a CSV file containing calculated spanwise data including chord, thickness, twist, and area values",
"Calculates t... | {
"invocation": "cli",
"argv": "blade_analysis.py --input <stl_path> --output <dir>",
"stdin": "none",
"stdout": "Summary of processed stations and generated files",
"stderr": "Error messages regarding parsing failures",
"exit_codes": {
"0": "analysis completed successfully",
"1": "input/output erro... | numerical | {
"year": 2025,
"platform": "generic",
"language": "Python",
"constraints": [
"requires numpy for numerical operations",
"requires matplotlib for figure generation",
"requires a mesh processing library for STL parsing"
]
} | medium | 1,200 | [
"openFOAM",
"SU22",
"Ansys",
"cad-query"
] | [
"aerodynamics",
"thermal",
"hydrogen",
"geometry",
"structural-analysis"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "1225zisu-lab__hydrogen-turbine-blade-analysis",
"archive_path": "github/github/1225zisu-lab__hydrogen-turbine-blade-analysis",
"posting_year": 2025
} | ri-2d43779be92d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 1,
"observed": 1,
"strong": 1,
"attempts": 7
} |
turbine-thermal-mlops | This system is a comprehensive MLOps framework designed for monitoring the thermal health of gas turbine blades. Engineers need to predict blade surface temperatures and classify operational risks (safe, warning, or critical) to prevent catastrophic mechanical failure. The program integrates data engineering for sensor... | [
"Processes raw turbine sensor data to engineer features based on load_pct, t_inlet_K, and t_coolant_K ratios",
"Applies feature scaling to input variables to ensure numerical stability during model training, verifying output is scaled to a standard range",
"Trains a regression model to predict blade surface tem... | {
"invocation": "cli",
"argv": "python src/data/features.py | python src/models/train.py | python orchestration/training_flow.py | python orchestration/batch_prediction_flow.py",
"stdin": "none",
"stdout": "Training logs, MLflow metrics, and JSON prediction results",
"stderr": "Error logs from FastAPI or Pref... | ai | {
"year": 2025,
"platform": "Linux",
"language": "Python 3.10+",
"constraints": [
"requires an active MLflow tracking server",
"requires a Prefect backend for orchestration",
"requires Docker runtime for containerization"
]
} | hard | 1,800 | [
"turbine-thermal-mlops",
"scikit-learn",
"fastapi",
"mlflow",
"prefect"
] | [
"mlops",
"thermal-analysis",
"predictive-maintenance",
"api",
"orchestration"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "1225zisu-lab__turbine-thermal-mlops",
"archive_path": "github/github/1225zisu-lab__turbine-thermal-mlops",
"posting_year": 2025
} | ri-efd917be2159 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
airocket | AIRocket is a sophisticated computational framework designed for optimizing 2D NACA airfoils using neural network surrogates. The system implements a complex pipeline that starts with generating steady-state viscous flow fields using the Multiple Relaxation Time Lattice Boltzmann Method (MRT-LBM). This data is used to ... | [
"Accepts a configuration file path via the --env argument to initialize solver and model parameters",
"Generates steady-state flow fields using D2Q9 MRT-LBM with Bouzidi curved boundary conditions for NACA geometries",
"Uses potential-flow-based initialization for the LBM solver, verifying that the iteration co... | {
"invocation": "cli",
"argv": "python run.py --env <config_yaml> [--mode <generate|train|eval|optimize|finetune>]",
"stdin": "none",
"stdout": "Progress logs, training metrics, and optimization history summaries",
"stderr": "Numerical warnings, convergence errors, or CUDA initialization messages",
"exit_co... | numerical | {
"year": 2026,
"platform": "Desktop with CUDA support",
"language": "Python 3.12",
"constraints": [
"Requires CUDA for performant LBM solving and Transformer training",
"High memory overhead for large-grid tensor operations",
"Requires FP32 precision for physical residual calculations"
]
} | hard | 3,500 | [
"black-box CFD solvers",
"high-level aerodynamic optimization libraries",
"pre-built LBM frameworks"
] | [
"cfd",
"lbm",
"neural-networks",
"naca",
"optimization"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "1os3__AIRocket",
"archive_path": "github/github/1os3__AIRocket",
"posting_year": 2026
} | ri-b333c94f36eb | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "pip-failed",
"attempts": 2
} |
zisaafvm | ZisaFVM is a specialized numerical framework designed to implement finite volume methods for solving complex partial differential equations on unstructured meshes. Unlike standard grid-based solvers, this tool handles arbitrary geometries, making it essential for industrial fluid dynamics simulations like aerospace or ... | [
"Parses an unstructured mesh input from a file, defining vertices, edges, and cells with connectivity",
"Outputs barycentric coordinates and cell index for a given point in a 2D element to stdout when in query mode",
"Computes HLLC numerical flux across cell interfaces using primitive variables defined in § Par... | {
"invocation": "cli",
"argv": "zisaafvm <config_file> <mesh_file> [query]",
"stdin": "none",
"stdout": "simulation progress statistics and convergence residuals, and query results",
"stderr": "error messages for mesh validation failures",
"exit_codes": {
"0": "simulation completed successfully",
"1... | numerical | {
"year": 2021,
"platform": "not specified",
"language": "C++",
"constraints": [
"requires MPI for parallel domain decomposition",
"requires HDF5 or NetCDF for scientific I/O"
]
} | hard | 2,800 | [
"OpenFOAM",
"SU2",
"PetSc",
"Deal.II"
] | [
"finite-volume",
"unstructured-mesh",
"cfd",
"high-order-methods"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "1uc__ZisaFVM",
"archive_path": "github/github/1uc__ZisaFVM",
"posting_year": 2021
} | ri-c2965995d68f | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 3
} |
stashmap | The stashmap package is a utility designed for managing the configurations of the Unified Model (UM), specifically targeting the STASH system. In atmospheric modeling, UM configurations use namelist files (like rose-app.conf) which are notoriously difficult to edit manually at scale. This tool bridges the gap by parsin... | [
"Parse a UM namelist file and identify specific section types: umstash_streq, umstash_domain, umstash_time, umstash_use, and nlstcall_pp.",
"Map identified sections to their respective internal class instances (Variable, DomainProfile, TimeProfile, UseProfile, OutputStream).",
"Provide a function that returns a... | {
"invocation": "library",
"argv": "",
"stdin": "none",
"stdout": "summary reports of parsed sections",
"stderr": "error messages during parsing or file operations",
"exit_codes": {
"0": "success",
"1": "file access error or parse failure"
},
"files": [
"reads .conf namelist",
"writes .c... | tooling | {
"year": 2025,
"platform": "not specified",
"language": "Python",
"constraints": [
"Python 3.8+ required"
]
} | medium | 800 | [
"any pre-existing UM configuration parsers",
"UM-specific STASH libraries"
] | [
"meteorology",
"namelist",
"csv-processing",
"config-management"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "21centuryweather__stashmap",
"archive_path": "github/github/21centuryweather__stashmap",
"posting_year": 2025
} | ri-58009634ae64 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 5
} |
wind-farm-ids | This program implements a research framework for intrusion detection within wind farm OT/SCADA systems. It addresses the challenge of detecting cyber threats in critical infrastructure by merging network flow data with physical turbine operational telemetry. The system supports two primary experimental paths: zero-shot... | [
"Accepts a command-line flag to switch between experiment A (anomaly) and experiment B",
"Parses CSV files containing network flow statistics (65 features) including TCP flags and flow rates",
"Parses telemetry CSV files containing 18 IEC 61400-25 signals such as rotor speed and tower displacement",
"Calculat... | {
"invocation": "cli",
"argv": "python code/reproduce_experiment.py --experiment [A|B] --model [xgboost|all]",
"stdin": "none",
"stdout": "Summary of performance metrics (AUROC, F1, AUPRC, Precision, Recall)",
"stderr": "Error messages regarding file paths or data dimensions",
"exit_codes": {
"0": "Exec... | ai | {
"year": 2025,
"platform": "Linux/Windows",
"language": "Python 3.11",
"constraints": [
"Requires minimum 8GB RAM for large-scale matrix operations",
"Optional CUDA support for neural network model variants"
]
} | medium | 1,200 | [
"scapy-for-parsing",
"prebuilt-intrusion-frameworks",
"black-box-pcap-solver"
] | [
"security",
"wind-energy",
"machine-learning",
"intrusion-detection",
"scada"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "24061989__Intrusion-Detection-Wind-Power-Plants",
"archive_path": "github/github/24061989__Intrusion-Detection-Wind-Power-Plants",
"posting_year": 2025
} | ri-5eb7a1a59b02 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "pip-failed",
"attempts": 2
} |
fea-gen-design | This program is a comprehensive computational mechanics engine designed for high-performance engineering design, specifically targeting automotive and nuclear components. It implements a layered Finite Element Analysis (FEA) stack starting from 1D linear bar elements to 2D linear elastic solvers using constant-strain t... | [
"Solve a 1D linear bar problem for axial deformation where nodal results match machine precision (1e-15) relative to analytical solutions",
"Implement a 2D plane-stress solver using CST elements that correctly accounts for Poisson coupling",
"Assemble a global stiffness matrix for a 2D mesh with 2 degrees of fr... | {
"invocation": "cli",
"argv": "python run_example.py config_file",
"stdin": "none",
"stdout": "Simulation results including nodal displacements, temperature fields, and convergence metrics",
"stderr": "Status updates and solver warnings",
"exit_codes": {
"0": "Success",
"1": "Solver divergence or i... | numerical | {
"year": 2026,
"platform": "unspecified",
"language": "Python",
"constraints": [
"Requires significant memory for global stiffness matrix assembly in large meshes",
"No external CFD or PDE framework libraries allowed"
]
} | hard | 2,500 | [
"FEniCS",
"SiPy",
"OpenFOAM",
"PyAnsys",
"FMS"
] | [
"FEA",
"topology-optimization",
"thermal-structural",
"generative-design",
"numerical-methods"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "24bricedj__FEA-to-generative-design",
"archive_path": "github/github/24bricedj__FEA-to-generative-design",
"posting_year": 2026
} | ri-b2ffd3057487 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 3,
"observed": 3,
"strong": 2,
"attempts": 3
} |
windctrl-validate | WindCtrl Validate is an automated validation and reporting toolkit designed for wind-turbine controller software. In the modern renewable energy industry, engineers must ensure that control algorithms protect multimillion-dollar turbines from structural failure while maximizing energy capture. This tool automates the v... | [
"Accept a CSV file path containing 11 mandatory channels: timestamp, wind_speed_mps, rotor_speed_rpm, generator_speed_rpm, generator_torque_nm, blade_pitch_deg, electrical_power_kw, tower_acceleration, nacelle_yaw_error, controller_state, and fault_flags.",
"Identify and flag signal integrity issues including NaN... | {
"invocation": "cli",
"argv": "windctrl-validate <input_csv> --output <pdf> [--fault-start <time> --duration <sec> --severity <val> --channel <name>]",
"stdin": "none",
"stdout": "Validation summary including score, test counts, and specific rule failure evidence",
"stderr": "Error messages regarding file pa... | numerical | {
"year": 2026,
"platform": "generic Linux",
"language": "Python 3.12",
"constraints": [
"requires pdflatex or ReportLab for reporting",
"must handle high-frequency (50Hz) telemetry data",
"must support pandas-based large dataframe operations"
]
} | medium | 1,200 | [
"fast",
"bladed",
"simulink"
] | [
"wind-energy",
"validation",
"telemetry",
"ci-cd"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "25Rohit25__Winder",
"archive_path": "github/github/25Rohit25__Winder",
"posting_year": 2026
} | ri-e254699a16ca | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
conduccion-2d-sor | This program solves the 2D steady-state heat conduction problem governed by the Laplace equation on a rectangular plate. It implements and compares three distinct iterative numerical methods—Jacobi, Gauss-Seidel, and Successive Over-Relaxation (SOR)—to determine temperature distributions. To ensure accuracy, the solver... | [
"Accepts a configuration for grid dimensions, boundary conditions, and solver method via command line arguments",
"Writes the Jacobi method results to the CSV, verifying the final state matches a reference run using only previous-iteration grid updates",
"Writes the Gauss-Seidel method results to the CSV, verif... | {
"invocation": "cli",
"argv": "conduccion2d <output_file> [options]",
"stdin": "none",
"stdout": "Summary of iterations and convergence status",
"stderr": "Error messages for invalid parameters",
"exit_codes": {
"0": "success",
"1": "invalid input or convergence failure"
},
"files": [
"outp... | numerical | {
"year": 2026,
"platform": "not specified",
"language": "C++17",
"constraints": [
"requires C++17 compiler",
"floating point precision requirements for double"
]
} | medium | 800 | [
"any-CFD-framework",
"PDE-libraries",
"black-box-solvers"
] | [
"heat-transfer",
"laplace-equation",
"finite-difference",
"iterative-methods",
"sor"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "261893-web__conduccion-2d-sor-cpp",
"archive_path": "github/github/261893-web__conduccion-2d-sor-cpp",
"posting_year": 2026
} | ri-7c12a5aab889 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 5,
"observed": 5,
"strong": 4,
"attempts": 2
} |
makeabaqus_inp | This Julia package is designed to generate ABAQUS input files (.inp) specifically tailored for the homogenization analysis of composite materials. In structural engineering, modeling a Representative Volume Element (RVE) is critical for determining the effective properties of complex microstructures. The tool automates... | [
"Provide a function write_3D_rve_inp that accepts a six-element Float64 tuple for the bounding box (x_min, y_min, z_min, x_max, y_max, z_max).",
"Accept a dictionary for inclusion data where keys are shape identifiers (e.g., 'CIRCLE', 'ELLIPSE') and values are matrices of geometric coordinates.",
"Process 'CIRC... | {
"invocation": "library",
"argv": "write_3D_rve_inp(bbox, incl_data; options=options)",
"stdin": "none",
"stdout": "none",
"stderr": "error messages regarding invalid geometry or file access",
"exit_codes": {
"0": "success",
"1": "invalid input parameters or write error"
},
"files": []
} | numerical | {
"year": 2022,
"platform": "multiple",
"language": "Julia",
"constraints": [
"requires gmsh for potential mesh generation",
"requires external .inp files for element connectivity"
]
} | medium | 800 | [
"AbaqusAPI",
"FEA-solver-black-box-libraries"
] | [
"finite-element",
"homogenization",
"composite-materials",
"abaqus"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "338rajesh__MakeAbaqusInputFile.jl",
"archive_path": "github/github/338rajesh__MakeAbaqusInputFile.jl",
"posting_year": 2022
} | ri-5c4148c04d93 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "julia-failed",
"attempts": 2
} |
pa-fem-forward | This program implements a two-dimensional finite-element method (FEM) forward model for photoacoustic imaging. It simulates the physical process starting from laser fluence absorption generating initial pressure, followed by the propagation of transient pressure waves through heterogeneous media like water and tissue. ... | [
"Accepts a configuration file path via the --config argument and an output directory via the --outdir argument",
"Parses Gmsh-generated meshes using meshio to verify physical groups and mesh quality",
"Computes the initial pressure field using a positive conservative mass-lumped L2 projection of a Gaussian sour... | {
"invocation": "cli",
"argv": "pa-fem solve --config <config.json> --outdir <path>",
"stdin": "none",
"stdout": "progress updates and simulation summary statistics",
"stderr": "error messages and solver warnings",
"exit_codes": {
"0": "simulation completed successfully",
"1": "configuration error, ... | numerical | {
"year": 2026,
"platform": "Linux/Unix",
"language": "Python 3.11+",
"constraints": [
"requires at least 8GB RAM for large mesh operations",
"requires libglu-mesa for visualization dependencies",
"must handle sparse matrix operations for FEM assembly"
]
} | hard | 2,400 | [
"scikit-fem",
"gmsh",
"meshio",
"k-wave"
] | [
"photoacousticics",
"fem",
"wave-propagation",
"numerical-simulation"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "341151719__photoacoustic-fem-forward",
"archive_path": "github/github/341151719__photoacoustic-fem-forward",
"posting_year": 2026
} | ri-4094f4228163 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
3view | The 3view program is designed for aeronautical engineers to visualize complex wing-body configurations through orthogonal projections. It processes data in the Langley Wireframe Geometry Standard (LaWGS) format, which defines aircraft structures using sets of vertices and edges. The tool transforms 3D coordinates into ... | [
"Prompts the user for a path to a .wgs input file via standard input",
"Parses the LaWGS file to extract vertex coordinates and edge-connectivity data",
"Generates a 'plan.gnu' file containing X and Y coordinates for the top-down projection",
"Generates a 'side.gnu' file containing X and Z coordinates for the... | {
"invocation": "cli",
"argv": "3view",
"stdin": "filename of the input .wgs file",
"stdout": "status messages during processing and completion",
"stderr": "error messages regarding file access",
"exit_codes": {
"0": "success",
"1": "file or parse error"
},
"files": [
"plan.gnu",
"side.g... | graphics | {
"year": 1985,
"platform": "not specified",
"language": "Fortran 90",
"constraints": [
"fixed-size arrays for vertex storage",
"no dynamic memory allocation"
]
} | medium | 600 | [
"modern graphics libraries",
"JSON/XML",
"dynamic memory allocation"
] | [
"aeronautics",
"wireframe",
"projection",
"gnuplot"
] | {
"source": "mined",
"msg_id": null,
"group": "pdas",
"date": null,
"signal": "announce",
"expansion": null,
"package": "3view",
"archive_path": "pdas/pdas/3view",
"posting_year": 1985
} | ri-5412539cd6cb | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 3,
"observed": 3,
"strong": 3,
"attempts": 3
} |
capillary-heat-solver | This numerical solver calculates the steady-state temperature distribution in 2D wall sections containing capillary heat exchangers. It utilizes the Finite Difference Method (FDM) to solve the heat equation ∇(λ2T) = 0. The program is designed for thermal engineers designing heat pump systems where low heating temperatu... | [
"Initialize a 2D grid representing the wall with specified dimensions nx by ny and assign thermal conductivity values to each cell",
"Output the 2D temperature distribution to stdout and verify the FDM scheme and harmonic mean conductivities using oracle O1",
"Implement a Neumann boundary condition at the botto... | {
"invocation": "cli",
"argv": "solver --config <params.json>",
"stdin": "none",
"stdout": "2D temperature matrix values and final adjusted water temperature",
"stderr": "convergence logs and error messages",
"exit_codes": {
"0": "success",
"1": "configuration or parse error",
"2": "failure to c... | numerical | {
"year": 2024,
"platform": "Jupyter Notebook",
"language": "Python",
"constraints": [
"requires scipy.sparse for large matrix operations",
"requires numpy for array-based grid calculations"
]
} | medium | 1,200 | [
"scipy.optimize",
"fenics",
"openfoam",
"black-box-thermal-solver"
] | [
"cfd",
"heat-transfer",
"fdm",
"sparse-matrix",
"capillary"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "4n515__steady_heat_solver",
"archive_path": "github/github/4n515__steady_heat_solver",
"posting_year": 2024
} | ri-bcc3ec4c0229 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
propfan-solver | This program is a conceptual design and simulation platform for a geared, single-rotor open propfan. At its core, it implements a Blade Element Momentum Theory (BEMT) solver developed from first principles to evaluate the aerodynamic performance of propellers. The tool must account for complex interactions between airc... | [
"Accepts a configuration JSON file path as a command-line argument to define propeller parameters",
"Parses blade geometry including diameter, number of blades, chord distribution, and twist angle distribution",
"Calculates the axial thrust and torque for a given set of inflow velocity (m/s) and rotational spee... | {
"invocation": "cli",
"argv": "propfan-solver <config.json>",
"stdin": "none",
"stdout": "performance table with thrust, torque, and efficiency per station",
"stderr": "error messages and convergence status logs",
"exit_codes": {
"0": "successful convergence",
"1": "invalid input parameters",
"... | numerical | {
"year": 2026,
"platform": "multi-platform",
"language": "Python",
"constraints": [
"must use SI units for all internal calculations",
"requires external 2D airfoil polar data for lookup"
]
} | hard | 1,200 | [
"XFOIL",
"OpenFOAM",
"black-box BEMT solvers",
"proprietary CFD software"
] | [
"aerodynamics",
"BEMT",
"propeller",
"simulation"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AAguilar-Aero__Propfan-Experimental",
"archive_path": "github/github/AAguilar-Aero__Propfan-Experimental",
"posting_year": 2026
} | ri-657cde72cab2 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
geo-map-viz | This tool is designed for the visualization of geological horizons and well data in both 2D and 3D environments. In petroleum and mining, engineers must visualize interpreted subsurface surfaces (horizons) relative to well locations to plan drilling accurately. The program processes spatial data from text files represe... | [
"Accepts 'plot2d' or 'plot3d' as the first command-line argument",
"Accepts a path to a map/horizon file as the second command-line argument",
"Prompt the user for a line number to skip and column indices for X, Y, and Z coordinates for the horizon file via stdin",
"Prompt the user for a line number to skip a... | {
"invocation": "cli",
"argv": "python geological_mapping_2d_3d.py [plot2d|plot3d] <horizon_path> [optional_well_path_or_folder]",
"stdin": "interactive prompts for column indices and skip lines",
"stdout": "status messages during plot generation",
"stderr": "error messages regarding file access or parsing fa... | graphics | {
"year": 2024,
"platform": "generic",
"language": "Python 3.9",
"constraints": [
"requires pyvista for 3D rendering",
"requires matplotlib for 2D plotting",
"requires pandas and numpy for data manipulation"
]
} | medium | 800 | [
"geological-modeling-framework",
"petrel-api-wrapper",
"black-box-mesh-generator"
] | [
"geology",
"subsurface",
"3d-visualization",
"well-tracking"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AB-Blue__Geological-Map-Visualization-2D-3D-view",
"archive_path": "github/github/AB-Blue__Geological-Map-Visualization-2D-3D-view",
"posting_year": 2024
} | ri-5f6268f8360d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 1,
"observed": 1,
"strong": 1,
"attempts": 2
} |
ctstr-reaction-sim | This program simulates and analyzes the chemical reaction between sodium thiosulfate and hydrogen peroxide within a continuous stirred-tank reactor (CSTR). The core logic involves solving a system of coupled ordinary differential equations (ODEs) representing the concentration changes of sodium thiosulfate, hydrogen pe... | [
"Accepts a configuration file via command-line argument defining parameters: V, F, UA, rho, dH, Cp, k0, E, R, Ti, Tj, Cai, Cbi, Cpi.",
"Solves the four coupled ODEs for Ca, Cb, Cp, and T using a numerical integration method like Runge-Kutta 4th order.",
"Calculates eigenvalues of the Jacobian; reports if the st... | {
"invocation": "cli",
"argv": "ctstr-reaction-sim <config.json>",
"stdin": "none",
"stdout": "CSV formatted data containing time, Ca, Cb, Cp, T",
"stderr": "Error messages and convergence status",
"exit_codes": {
"0": "success",
"1": "input or computation error"
},
"files": [
"config.json"
... | numerical | {
"year": 2023,
"platform": "Generic OS",
"language": "Python (Jupyter)",
"constraints": [
"requires numerical solver for ODEs",
"requires linear algebra library for eigenvalue decomposition"
]
} | medium | 1,200 | [
"scipy.integrate.odeint",
"cantera",
"reaction-frameworks"
] | [
"chemical-engineering",
"ode",
"thermal",
"simulation"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "ABCO2__CHE2410-Project-2",
"archive_path": "github/github/ABCO2__CHE2410-Project-2",
"posting_year": 2023
} | ri-e3c849530d78 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
propulsion-lab | PropulsionLab is a high-fidelity thermodynamic simulator designed for educational and preliminary analysis of gas-turbine propulsion systems. It implements station-based 1D cycle models for various engine architectures, including turbojets, turbofans, turboprops, ramjets, and scramjets. The solver calculates states acr... | [
"Calculate ambient atmospheric pressure, temperature, and density based on altitude_m using the ISA atmosphere model for troposphere (up to 11km) and stratosphere.",
"Compute freestream stagnation properties (T0, P0, V0) given mach and atmospheric state via stdin.",
"Solve a steady-state turbojet cycle by calcu... | {
"invocation": "cli",
"argv": "python solver.py --config config.json",
"stdin": "JSON object containing engine parameters or mission profiles",
"stdout": "JSON object containing station-table data, performance metrics, and convergence reports",
"stderr": "Error messages and convergence warnings",
"exit_cod... | numerical | {
"year": 2026,
"platform": "multi-platform",
"language": "Python",
"constraints": [
"Requires NumPy for numerical operations",
"Requires Pydantic v2 for data validation",
"Optional dependency on Cantera for real-gas effects"
]
} | hard | 2,800 | [
"FastAPI",
"Three.js",
"black-box CFD solvers",
"pre-built aerospace optimization libraries"
] | [
"aerospace",
"thermodynamics",
"simulation",
"optimization"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AINESHXD__propulsion-lab",
"archive_path": "github/github/AINESHXD__propulsion-lab",
"posting_year": 2026
} | ri-d1836d10cda0 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the example ran but no oracle value was found in its output",
"kind": "no_observed_value",
"run_status": "ran",
"attempts": 11
} |
twinguard | TwinGuard is a framework for reliability-aware revalidation of digital twins that have degraded. In industrial settings, a digital twin calibrated on a healthy plant becomes inaccurate as the plant undergoes fouling or mechanical wear. TwinGuard solves the problem of determining if a proposed control action can be cert... | [
"Fit a latent linear-Gaussian state-space model using Expectation-Maximization on residuals calculated from observed plant outputs and nominal twin predictions",
"Generate a bootstrap ensemble of discrepancy models filtered on the historical observed plant data",
"Propagate the ensemble as coherent trajectory s... | {
"invocation": "cli",
"argv": "python scripts/reproduce_main_results.py --rerun <experiment> --workspace <path>",
"stdin": "none",
"stdout": "Headline tables of results and reproducibility audit status",
"stderr": "Warnings regarding hash mismatches in artifacts",
"exit_codes": {
"0": "Success and audi... | numerical | {
"year": 2026,
"platform": "specified",
"language": "Python 3.10",
"constraints": [
"Requires Python 3.10+",
"Requires high memory for bootstrap ensemble propagation",
"Strict SHA-256 hashing for artifact integrity"
]
} | hard | 2,800 | [
"scikit-learn",
"casadi",
"pyomo",
"black-box-cfd-solver"
] | [
"digital-twin",
"conformal-prediction",
"state-space",
"safety-certification",
"thermal-systems"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AISL-at-Imperial-College-London__twinguard",
"archive_path": "github/github/AISL-at-Imperial-College-London__twinguard",
"posting_year": 2026
} | ri-02ee0654884e | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 4,
"observed": 4,
"strong": 4,
"attempts": 2
} |
conflict-emissions | This program performs Monte Carlo simulations to quantify greenhouse gas (GHG) emission uncertainty in conflict zones. Unlike standard industrial monitoring, conflict data is often obscured by infrastructure destruction, military secrecy, and satellite limitations, making single-point estimates unreliable. The tool use... | [
"Accepts the number of Monte Carlo iterations as a command-line argument (defaulting to 100,000)",
"Defines six emission sources with mean (MtCO2e/yr) and coefficient of variation (CV) specified in § Parameters",
"Generates samples for each source using a lognormal distribution based on the mean and CV in § Par... | {
"invocation": "cli",
"argv": "conflict-emissions [iterations]",
"stdin": "none",
"stdout": "Mean emissions, P5 value, P95 value, P95/P5 ratio, and sensitivity analysis per source",
"stderr": "Error messages regarding input",
"exit_codes": {
"0": "Simulation successful",
"1": "Invalid input paramet... | numerical | {
"year": 2026,
"platform": "cross-platform",
"language": "Python",
"constraints": [
"requires numpy for vectorized Monte operations",
"requires scipy for lognormal distribution sampling"
]
} | medium | 650 | [
"jupyter",
"pandas",
"specialized-cdf-libraries"
] | [
"monte-carlo",
"ghg-accounting",
"climate-modeling",
"uncertainty-quantification"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AKKK24X7__conflict-emission-uncertainty",
"archive_path": "github/github/AKKK24X7__conflict-emission-uncertainty",
"posting_year": 2026
} | ri-9318af99d23d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
te-module-cooling | This program implements an analytical performance model for tellurium-free thermoelectric modules based on MgAgSb/Mg₃(Sb,Bi)₂ materials. Unlike 3D finite element simulations, this tool uses closed-form single-couple equations to evaluate the efficiency of solid-state cooling devices. It allows engineers to analyze how ... | [
"Calculate the net Seebeck coefficient by subtracting the n-type Seebeck value from the p-type Seebeck value",
"Compute module electrical resistance based on the number of couples, leg resistivity, length, area, and specific contact resistance",
"Compute module thermal conductance using the thermal conductivity... | {
"invocation": "cli",
"argv": "python te_module_analysis.py --config config.json",
"stdin": "none",
"stdout": "Table of calculated performance metrics and paths to generated PNG figures",
"stderr": "Error messages regarding convergence failure or invalid parameters",
"exit_codes": {
"0": "Analysis comp... | numerical | {
"year": 2026,
"platform": "generic",
"language": "Python 3.8+",
"constraints": [
"Requires numpy for matrix operations",
"Requires matplotlib for data visualization"
]
} | medium | 800 | [
"comsol",
"ansys",
"pde-solver",
"thermoelectric-frameworks"
] | [
"thermoelectric",
"cooling",
"analytical-model",
"optimization"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AKKK24X7__te-module-cooling-analysis",
"archive_path": "github/github/AKKK24X7__te-module-cooling-analysis",
"posting_year": 2026
} | ri-eb1fd3d78572 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 4,
"observed": 4,
"strong": 3,
"attempts": 2
} |
hymor-mesh | The HYMOR mesh generator is a specialized tool designed for hypersonic flow analysis. In hypersonics, standard Cartesian grids fail to capture the complex physics of bow shocks and the sharp curvatures of blunt bodies. This program generates structured curvilinear meshes for specific geometric primitives including blun... | [
"Accept a configuration structure defining the geometry type (wedge, cone, cylinder, sphere, or blunt variant)",
"Accept parameters for number of grid points in the radial, axial, and azimuthal/streamwise directions",
"Generate a structured 3D coordinate array (x, y, z) for a cylinder geometry based on radius a... | {
"invocation": "library",
"argv": "hymor_mesh(config_struct)",
"stdin": "none",
"stdout": "mesh statistics and generation status",
"stderr": "error messages regarding geometric constraints or mapping failures",
"exit_codes": {
"0": "mesh generation successful",
"1": "invalid geometric parameters or... | numerical | {
"year": 2026,
"platform": "MATLAB R2023b",
"language": "MATLAB",
"constraints": [
"requires Optimization Toolbox for nonlinear shock fitting",
"requires Curve Fitting Toolbox for spline-based mappings"
]
} | medium | 1,200 | [
"black-box CFD solvers",
"OpenFO or Gmsh libraries",
"HYMOR core solver modules"
] | [
"mesh-generation",
"hypersonic",
"curvilinear",
"structured-grid"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "ALD-Lab__HYMOR#mesh-generator",
"archive_path": "github/github/ALD-Lab__HYMOR#mesh-generator",
"posting_year": 2026
} | ri-689a7bb972da | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: MATLAB",
"kind": "skipped"
} |
modal-stability-solver | The Modal Stability Solver performs global modal analysis on high-enthalpy hypersonic flows by solving the eigenvalue problem for the linearized Navier-Stokes operator. It identifies disturbances that grow exponentially over time. This task is complex because it requires linearizing discrete nonlinear operators on curv... | [
"Read base-flow solution data and mesh geometry from the specified input files",
"Construct the linearized Navier-Stokes operator and verify the matrix action on a test vector against the reference run of the channel_flow case",
"Implement an Implicitly Restarted Arnoldi Method to find the k eigenvalues with th... | {
"invocation": "cli",
"argv": "modal_solver <input_file> <output_file>",
"stdin": "none",
"stdout": "list of eigenvalues and growth rates",
"stderr": "iteration progress and convergence-status messages",
"exit_codes": {
"0": "analysis completed successfully",
"1": "convergence failed or input error... | numerical | {
"year": 2026,
"platform": "MATLAB",
"language": "MATLAB",
"constraints": [
"requires GPU for accelerated spectral transformation",
"requires Optimization and Curve Fitting toolboxes",
"memory limited matrix operations"
]
} | hard | 2,500 | [
"any existing CFD framework",
"black-box eigenvalue solver libraries",
"HYMOR internal stability modules"
] | [
"cfd",
"eigenvalue",
"hypersonic",
"navier-stokes"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "ALD-Lab__HYMOR#modal-stability-solver",
"archive_path": "github/github/ALD-Lab__HYMOR#modal-stability-solver",
"posting_year": 2026
} | ri-f6b64cf19cd3 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: MATLAB",
"kind": "skipped"
} |
it-pi | IT-PI is an information-theoretic, data-driven framework designed to identify the most predictive dimensionless variables in physical systems. In dimensional analysis, the Buckingham-pi theorem provides multiple ways to construct dimensionless groups; IT-PI resolves this ambiguity by using the irreducible error theorem... | [
"Implement a function `calc_basis` that takes a dimension matrix and the required basis count to return a set of matrices representing the null space",
"Implement a core `main` function that accepts an input matrix X, an output matrix Y, and pre-calculated basis matrices",
"The core solver must support the 'kra... | {
"invocation": "library",
"argv": "IT_PI.main(X, Y, basis_matrices, ...)",
"stdin": "none",
"stdout": "dictionary-like object with analysis results",
"stderr": "error messages for convergence or convergence failures",
"exit_codes": {
"0": "successful execution",
"1": "dimension matrix mismatch or r... | numerical | {
"year": 2025,
"platform": "Jupyter",
"language": "Python",
"constraints": [
"requires numpy for linear algebra",
"requires scipy for special functions like erf",
"requires pandas for data handling",
"requires cma for potential optimization tasks"
]
} | hard | 1,200 | [
"scikit-learn",
"SymPy",
"any CFD-specific framework"
] | [
"dimensional-analysis",
"information-theory",
"fluid-dynamics",
"machine-learning"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "ALD-Lab__IT_PI",
"archive_path": "github/github/ALD-Lab__IT_PI",
"posting_year": 2025
} | ri-9ac84881a879 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
oceanosse | OceanOSSE is a specialized Python toolbox designed for performing Observing System Simulation Experiments (OSSEs) within ocean general circulation models. In climate science, researchers need to test how sparse observational data, such as Argo floats or moored moorings, can improve the prediction of ocean states. This ... | [
"Accepts a TOML configuration file via a command-line argument to define the experiment parameters",
"Load ocean model variables from NetCDF files using specified dimensions and coordinates defined in the configuration",
"Generate a synthetic sparse dataset by sampling the model truth fields based on the provid... | {
"invocation": "cli",
"argv": "oceanosse run [config_file] [--dry-run]",
"stdin": "none",
"stdout": "Progress updates and summary of the experiment results",
"stderr": "Error messages regarding file access or configuration",
"exit_codes": {
"0": "success",
"1": "configuration error or IO failure"
... | numerical | {
"year": 2025,
"platform": "generic",
"language": "Python 3.13",
"constraints": [
"requires xarray for multi-dimensional array handling",
"requires netcdf4 for file I/O"
]
} | medium | 1,200 | [
"pre-built ocean model frameworks",
"black-box data assimilation solvers",
"OceanOSSE internal package modules"
] | [
"oceanography",
"osse",
"regridding",
"synthetic data"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AMOCcommunity__OceanOSSE",
"archive_path": "github/github/AMOCcommunity__OceanOSSE",
"posting_year": 2025
} | ri-a9939ff32018 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
stm | The Slurry Temperature Model (STM) is a numerical tool designed to predict the temperature of animal manure or similar materials stored in outdoor tanks. It utilizes a zero-dimensional lumped capacitance approach to simulate heat exchange between the slurry and its environment (air, soil, walls, and floor). The model a... | [
"Accepts a 1-10 character run ID as the first command-line argument to identify the simulation",
"Reads a mandatory parameter file (pars.txt) containing physical constants like density, specific heat, and resistance terms",
"Reads a user parameter file (user_pars.txt) to override default values and define speci... | {
"invocation": "cli",
"argv": "stm <ID> <par_file_name> <user_par_file_name> [weather_file] [levelfile]",
"stdin": "none",
"stdout": "Simulation status updates or usage message if arguments are missing",
"stderr": "Errors regarding file parsing or calculation",
"exit_codes": {
"0": "success",
"1": ... | numerical | {
"year": 2020,
"platform": "Linux, macOS, Windows",
"language": "Fortran 90",
"constraints": [
"requires Fortran runtime libraries on Windows",
"fixed-row structure for parameter files"
]
} | medium | 800 | [
"any 3D CFD framework",
"black-box PDE solver",
"external thermal modeling libraries"
] | [
"heat-transfer",
"agricultural",
"numerical-modeling",
"slurry"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AU-BCE-EE__STM",
"archive_path": "github/github/AU-BCE-EE__STM",
"posting_year": 2020
} | ri-b73c05251f4d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 3,
"observed": 3,
"strong": 2,
"attempts": 2
} |
gisistwmm5 | GisToSWMM5 is a specialized tool designed to automate the generation of Storm Water Management Model (SWMM5) descriptions from geographic information data. Manually creating urban subcatchments is a tedious process prone to error, especially when dealing with complex drainage networks. This program processes elevation ... | [
"Accepts a script-like input via command line containing 25 paths to raster files, CSV tables, and an output filename.",
"Parses .asc raster files for elevation, flow direction, and land-use, ensuring they share identical dimensions and cell sizes.",
"Validates that provided CSV tables (catchments, junctions, c... | {
"invocation": "cli",
"argv": "gisistwmm5 [dem_asc] [flow_asc] [landuse_asc] [catchment_csv] [junction_csv] [conduit_csv] [outfall_csv] [header_csv] [evap_csv] [temp_csv] [snow_csv] [rainage_csv] [inflow_csv] [timeseries_csv] [pump_csv] [pump_curve_csv] [storage_csv] [dwf_csv] [pattern_csv] [losses_csv] [crosssect... | numerical | {
"year": 2016,
"platform": "Windows/Linux",
"language": "C++",
"constraints": [
"Large-scale raster processing may exceed available RAM",
"Requires manual parsing of ASCII .asc header formats"
]
} | hard | 2,500 | [
"SWMM-specific API libraries",
"GDAL/OGR",
"black-box PDE solvers"
] | [
"hydrology",
"gis",
"swmm",
"raster"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AaltoUrbanWater__GisToSWMM5",
"archive_path": "github/github/AaltoUrbanWater__GisToSWMM5",
"posting_year": 2016
} | ri-d2ecc2b6f66a | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 3,
"observed": 2,
"strong": 2,
"attempts": 2
} |
carbon-passport | Carbon Passport is an automated carbon footprint estimation platform designed for MSMEs and small manufacturing facilities facing international sustainability mandates like EU CBAM and GHG Protocol. The system replaces unreliable manual data collection by processing utility bills via OCR and auditing physical operation... | [
"Provide a FastAPI-based REST API that accepts uploads for utility bills, images, and video clips",
"Parse utility bills using OCR to extract energy consumption units (kWh, Litres, etc.) with an associated confidence score",
"Implement a YOLOv8-based vision pipeline to identify machinery and detect smoke densit... | {
"invocation": "cli",
"argv": "uvicorn backend.api.main:app --host 0.0.0.0 --port 8000",
"stdin": "none",
"stdout": "JSON responses for API endpoints and status logs",
"stderr": "Server logs and error tracebacks",
"exit_codes": {
"0": "Server started successfully",
"1": "Configuration error or star... | numerical | {
"year": 2026,
"platform": "Windows, macOS, Linux",
"language": "Python 3.12",
"constraints": [
"Minimum 4 GB RAM required",
"CPU-only inference supported for vision models",
"Requires external API keys for generative AI features"
]
} | hard | 2,800 | [
"black-box carbon-solvers",
"general-pde-frameworks",
"manual-spreadsheet-calculation"
] | [
"carbon-footprint",
"ocr",
"computer-vision",
"sustainability",
"fastapi"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Aayush-Bajaj__code-2.0",
"archive_path": "github/github/Aayush-Bajaj__code-2.0",
"posting_year": 2026
} | ri-da92c2c24001 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "pip-failed",
"attempts": 2
} |
soufflerie | Soufflerie is a reproducible workflow for computational fluid dynamics (CFD) that integrates D2Q9 lattice Boltzmann solvers with machine learning surrogate models. It is designed for aerospace engineers and researchers who need to validate flow models against high-fidelity simulations while maintaining immutable data l... | [
"Implements 'soufflerie version --json' to output package and Python versions as a JSON object to stdout",
"Accepts 'soufflerie solve' with --config and --output arguments to execute the solver",
"Provides 'soufflerie dataset validate' with a manifest path to verify Parquet schemas and logical dataset digests",... | {
"invocation": "cli",
"argv": "soufflerie [COMMAND] [OPTIONS]",
"stdin": "none",
"stdout": "human-readable results or schema-v1 JSON via --json flag",
"stderr": "schema-v1 JSON records on failure",
"exit_codes": {
"0": "success",
"2": "usage/config error",
"3": "numerical domain error",
"4"... | numerical | {
"year": 2026,
"platform": "multi-platform",
"language": "Python 3.11",
"constraints": [
"requires lazy loading of CUDA and ML libraries",
"must support float32 precision model weights",
"must handle Parquet-based dataset storage"
]
} | hard | 2,800 | [
"black-box-cfd-solver",
"open-foam-api",
"torch-internal-materialization"
] | [
"cfd",
"lattice-boltzmann",
"surrogate",
"reproducibility"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AbdelStark__soufflerie",
"archive_path": "github/github/AbdelStark__soufflerie",
"posting_year": 2026
} | ri-f5fb56b733cc | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "no usable run recipe",
"kind": "sandbox",
"run_status": "no-recipe",
"attempts": 10
} |
meca-solve | MECA_SOLUDE is a high-performance finite element method (FEM) solver designed for structural mechanics and thermal analysis. It solves complex engineering problems involving linear static analysis, nonlinear deformations, and implicit/explicit dynamics. The program handles a wide variety of elements ranging from 3D con... | [
"Parses Abaqus-style .inp input files containing nodes, elements, material properties, boundary conditions, and loads",
"Computes the global stiffness matrix for a 3D mesh using Hex8 elements and Gauss quadrature, verifying the L2 norm of the matrix against a reference run",
"Applies Dirichlet boundary conditio... | {
"invocation": "cli",
"argv": "meca-solve <input_file> <output_file>",
"stdin": "none",
"stdout": "converged=%d max|u|=%.4e max_vM=%.4e\n",
"stderr": "solver progress and convergence statistics",
"exit_codes": {
"0": "solve completed successfully",
"1": "convergence failed",
"2": "input file pa... | numerical | {
"year": 2026,
"platform": "generic",
"language": "C++17",
"constraints": [
"CUDA 11.0+ for GPU kernels",
"OpenMP for threading",
"Minimum 4GB RAM for large meshes"
]
} | hard | 2,800 | [
"meca_so",
"OpenEniCS",
"FreeFOAM",
"black-box FEM solvers"
] | [
"fem",
"structural-mechanics",
"cuda",
"nonlinear-solver"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Abdessd__MECA_SOLUDE",
"archive_path": "github/github/Abdessd__MECA_SOLUDE",
"posting_year": 2026
} | ri-6856c5e075f7 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
darcy-cpp | This program implements a high-performance solver for 2D Darcy flow, specifically solving the elliptic PDE -div(a grad u) = f using a finite-volume method. It is designed for media with highly discontinuous permeability that vary by several orders of magnitude. To handle large-scale grids where standard iterative solve... | [
"Accept a 2D array of permeability values 'a' and a source term 'f' via the API",
"Solve the Darcy equation with Dirichlet boundary conditions (u=0) on the domain edges",
"Use harmonic averaging to calculate effective permeability between adjacent cells",
"Verify the matrix-free 5-point stencil by comparing t... | {
"invocation": "cli",
"argv": "darcy solve --grid <int> --seed <int> | darcy dataset --n-samples <int> --grid <int> -o <path> | darcy bench <grids...> | darcy info",
"stdin": "none",
"stdout": "solver diagnostics, benchmark tables, or build info",
"stderr": "error messages for invalid parameters or failures"... | numerical | {
"year": 2026,
"platform": "not specified",
"language": "Python",
"constraints": [
"OpenMP for parallelization",
"pybind11 for bindings",
"C++17 standard required"
]
} | hard | 2,500 | [
"scipy.sparse.linalg",
"petsc",
"any-black-box-pde solver"
] | [
"darcy-flow",
"multigrid",
"finite-gradient",
"matrix-free",
"finite-volume"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AbdullahRasheed45__darcy-cpp",
"archive_path": "github/github/AbdullahRasheed45__darcy-cpp",
"posting_year": 2026
} | ri-97c21b3459ec | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
plasmapinn | This program implements a Physics-Informed Neural Network (PINN) framework designed to model and validate turbulent transport in magnetized plasmas. Specifically, it enables the quantitative comparison between drift-reduced Braginskii two-fluid theory and electromagnetic gyrokinetic modeling. By utilizing deep learning... | [
"Initialize a neural network with an architecture defined by layers and units, mapping spatial-temporal coordinates to plasma variables",
"Compute the physics loss using the drift-reduced Braginskii two-fluid equations via automatic differentiation of the model",
"Compute the physics loss for electromagnetic gy... | {
"invocation": "cli",
"argv": "plasmapinn --config config.yaml --data plasma_points.csv",
"stdin": "none",
"stdout": "training logs with loss values per epoch",
"stderr": "errors regarding configuration or convergence failure",
"exit_codes": {
"0": "training completed successfully",
"1": "convergen... | numerical | {
"year": 2021,
"platform": "generic OS",
"language": "Python",
"constraints": [
"requires GPU-enabled environment for tensor operations",
"requires significant memory for high-order derivatives"
]
} | hard | 1,800 | [
"torch",
"tensorflow",
"jax",
"pde"
] | [
"plasma-physics",
"deep-learning",
"physics-solver",
"fusion"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AbhilashMathews__PlasmaPINNtheory",
"archive_path": "github/github/AbhilashMathews__PlasmaPINNtheory",
"posting_year": 2021
} | ri-b60e7baa29dd | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
curvpack | curvpack is a specialized numerical library designed for the geometric analysis of manifold triangular surface meshes. It focuses on the estimation of curvature properties and Laplace-Beltrami operators, which are critical in fields like computer graphics, physical simulation, and shape analysis. The tool implements mu... | [
"Generate a spherical surface mesh starting from an icosahedron with a refinement level of Parameters, where the face count increases quadratically.",
"Accept a triangular mesh defined by vertex coordinates, face indices, and vertex normals via the input format.",
"Calculate the mean and Gaussian curvature for ... | {
"invocation": "library",
"argv": "",
"stdin": "none",
"stdout": "none",
"stderr": "error messages regarding mesh topology or invalid parameters",
"exit_codes": {
"0": "success",
"1": "invalid mesh input or parameters"
},
"files": []
} | graphics | {
"year": 2019,
"platform": "general purpose",
"language": "Python",
"constraints": [
"requires numpy for linear algebra operations",
"memory usage scales with mesh density and refinement levels"
]
} | hard | 1,800 | [
"open3d",
"libigl",
"scipyspatial",
"trimesh"
] | [
"numerical",
"mesh",
"geometry",
"differential-geometry"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AbhilashReddyM__curvpack",
"archive_path": "github/github/AbhilashReddyM__curvpack",
"posting_year": 2019
} | ri-cec1b090c629 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
f1-propulsion-sim | This multi-physics simulator is designed to validate the performance of next-generation F1 internal combustion engines using advanced material science. The program models the mechanical-thermal behavior of carbon-carbon composite pistons integrated with functionally graded gyroid (FGM) lattice structures. It calculates... | [
"Simulates a 53-lap race at Monza covering a total distance of 307.03 km",
"Calculates gear shifts across an 8-speed seamless-shift envelope between 10000 and 15000 RPM",
"Evaluates thermal strain in a 5.3mm FGM gyroid matrix under 850 degree Celsius heat flux surges",
"Verifies that peak thermal strain remai... | {
"invocation": "cli",
"argv": "f1_colab_simulation.py [input_config_file]",
"stdin": "none",
"stdout": "Engineering logs, telemetry matrix, and analytical plot data",
"stderr": "Thermal strain warnings or parsing error messages",
"exit_codes": {
"0": "Simulation completed successfully",
"1": "Struc... | numerical | {
"year": 2026,
"platform": "Google Colab / Linux",
"language": "Python",
"constraints": [
"requires matplotlib for multi-panel plotting",
"requires numpy for FEA matrix operations"
]
} | hard | 1,800 | [
"black-box-solvers",
"open-source-fea frameworks",
"commercial-g-code-verifiers"
] | [
"CFD",
"multi-physics",
"thermal analysis",
"g-code",
"telemetry"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Abhishek1033ubuntu__High-Performance-F1-Propulsion-Optical-Computing-Simulator",
"archive_path": "github/github/Abhishek1033ubuntu__High-Performance-F1-Propulsion-Optical-Computing-... | ri-3db42c5d49bb | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 3,
"observed": 2,
"strong": 0,
"attempts": 3
} |
mssa_80-simulation | This program is an analytical conceptual design framework designed to evaluate the technical and economic viability of an 80-seat supersonic commercial airliner. It specifically targets mid-sized aircraft cruising at Mach 1.7 with a range of 4,200 nautical miles. The simulation engine models complex physical phenomena ... | [
"Execute the core simulation by running 'mssa_80_simulation.py' without mandatory arguments",
"Output compressible wave drag coefficients for- Mach numbers from Mach 0.8 to Mach 2.0 in stdout",
"Output thermal stagnation heating temperatures for altitudes between 55,000 and 60,000 feet in stdout",
"Output str... | {
"invocation": "cli",
"argv": "python mssa_80_simulation.py",
"stdin": "none",
"stdout": "analytical aero-thermal validation results",
"stderr": "error messages and status logs",
"exit_codes": {
"0": "successful simulation",
"1": "parameter error or convergence failure"
},
"files": []
} | numerical | {
"year": 2026,
"platform": "linux",
"language": "Python 3.8+",
"constraints": [
"requires Python 3.8 environment",
"requires high-precision floating point arithmetic"
]
} | medium | 1,200 | [
"any CFD framework",
"any pde solver library",
"black-box aerodynamic solvers"
] | [
"supersonic",
"aero",
"simulation",
"composite-materials"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Abhishek1033ubuntu__Project-MSSA-80",
"archive_path": "github/github/Abhishek1033ubuntu__Project-MSSA-80",
"posting_year": 2026
} | ri-628a6f5b181d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "regenerated record failed validation: interface:exit_codes<2 (interface.exit_codes must list at least two codes)",
"kind": "validator",
"run_status": "ran",
"attempts": 3,
"oracles": 4,
"observed": 4
} |
hydro-flow-cli | Hydro-Flow CLI is a specialized command-line tool designed for hydraulic simulation and geospatial analysis. Traditional hydrology often relies on heavy graphical interfaces, but this tool provides a headless engine for solving inverse hydraulic problems. It uses the Newton-Raphson method to determine optimal channel d... | [
"Accepts a 'design' command with --target-q and --max-depth flags to calculate minimum channel width using scipy optimization",
"Outputs 'OPTIMAL DESIGN FOUND' followed by the recommended width and excavation volume to stdout upon a successful design",
"Accepts a 'stress-test' command with --depth and --iterati... | {
"invocation": "cli",
"argv": "hydro [command] [flags]",
"stdin": "none",
"stdout": "simulation results and status messages",
"stderr": "errors and warnings",
"exit_codes": {
"0": "success",
"1": "error or convergence failure"
},
"files": [
"local_workspace/cross_section.png",
"local_wo... | numerical | {
"year": 2026,
"platform": "multi-platform",
"language": "Python",
"constraints": [
"requires Python 3.9+",
"requires scipy for optimization",
"requires matplotlib for plotting",
"requires numpy for vector operations"
]
} | medium | 1,200 | [
"HEC-RAS",
"OpenSWMM",
"black-box hydraulic solver APIs"
] | [
"hydrology",
"hydraulics",
"geospatial",
"monte-carlo",
"optimization"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AdMub__hydro-flow-cli",
"archive_path": "github/github/AdMub__hydro-flow-cli",
"posting_year": 2026
} | ri-9083a93aba6e | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "error: UnicodeDecodeError: 'utf-8' codec can't decode byte 0x94 in position 5: invalid start byte"
} |
wildfire-persistence | This program implements a predictive framework for determining whether a wildfire event will persist based on its initial satellite detection via MODIS. Standard satellite confidence scores are often biased by orbital viewing geometry rather than actual fire intensity; this system solves that by aggregating raw detecti... | [
"Parses raw MODIS active-fire detections and clusters them into 5km x 72h spatiotemporal complexes",
"Applies right-censoring correction to events at the first detection timestamp (t0) to prevent temporal bias",
"Interpolates 4-D ERA5 atmospheric variables (VPD, 10m wind speed, 2m temperature) onto event coordi... | {
"invocation": "cli",
"argv": "python wildfire_prediction.py",
"stdin": "none",
"stdout": "benchmark tables and progress status",
"stderr": "logs and error messages",
"exit_codes": {
"0": "execution completed successfully",
"1": "pipeline failure or data error"
},
"files": [
"fire_events.cs... | ai | {
"year": 2026,
"platform": "not specified",
"language": "Python 3.9+",
"constraints": [
"requires memory for 4D spatial interpolation",
"needs access to CDS API for ERA5 data"
]
} | hard | 2,400 | [
"any black-box wildfire solver",
"wildfire-specific clustering libraries"
] | [
"wildfire",
"satellite-sensing",
"machine-learning",
"meteorology"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Adithya-N-S__Wildfire-persistence-prediction",
"archive_path": "github/github/Adithya-N-S__Wildfire-persistence-prediction",
"posting_year": 2026
} | ri-54b48fdf768d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
airfoil-noise-analysis | This program is designed to analyze and model the NASA Airfoil Self-Noise dataset, which contains acoustic data from two and three-dimensional NACA 0012 airfoil sections tested in an anechoic wind tunnel. The goal is to process aerodynamic parameters—specifically frequency, angle of attack, chord length, free-stream ve... | [
"Reads a CSV-formatted dataset where the first line contains headers and subsequent rows contain six numerical values",
"Parses the first five columns as: Frequency, Angle of attack, Chord length, Free-stream velocity, and Suction side displacement thickness",
"Parses the sixth column as the Scaled sound pressu... | {
"invocation": "cli",
"argv": "airfoil-noise-analysis <input_csv>",
"stdin": "New aerodynamic parameters for prediction",
"stdout": "Descriptive statistics, model metrics, and predicted decibel values",
"stderr": "Error messages and progress logs",
"exit_codes": {
"0": "analysis successful",
"1": "... | ai | {
"year": 2020,
"platform": "Jupyter Notebook",
"language": "Python",
"constraints": [
"requires external libraries for numerical operations and machine learning",
"memory usage depends on dataset size scaling"
]
} | medium | 600 | [
"scikit-learn",
"tensorflow",
"pytorch",
"keras"
] | [
"aeroacoustic",
"acoustics",
"machine-learning",
"regression"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Aditya-Singla__Airfoil-Self-Noise",
"archive_path": "github/github/Aditya-Singla__Airfoil-Self-Noise",
"posting_year": 2020
} | ri-6de2f712c345 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
multiphysics-sim | This simulation suite provides high-fidelity numerical modeling for complex micro-scale devices and environmental engineering systems. It addresses three distinct physical domains: Atmospheric Water Harvesting (AWH), which requires coupled mass and heat transfer ODEs to model sorbent kinetics; MEMS Electrostatic Comb-D... | [
"Implement an AWH simulation solver using the RK45 adaptive integration method to calculate sorbent loading q(t) over a 24-hour cycle",
"The AWH module must account for solar irradiance of 800 W/m2 and an adsorption enthalpy of 2.8 MJ/kg in the energy balance",
"Implement a MEMS comb-drive solver that models th... | {
"invocation": "cli",
"argv": "python -m src.<module_name> [options]",
"stdin": "none",
"stdout": "simulation data points, metrics, and plot file paths",
"stderr": "numerical warnings and convergence errors",
"exit_codes": {
"0": "success",
"1": "simulation failure or invalid input"
},
"files":... | numerical | {
"year": 2026,
"platform": "unspecified",
"language": "Python 3.8+",
"constraints": [
"requires scipy for sparse solvers and ODE integration",
"requires numpy for array operations",
"requires matplotlib for output generation"
]
} | hard | 1,800 | [
"high-level cfd frameworks",
"black-box TCAD software",
"commercial multiphysics simulation libraries"
] | [
"numerical",
"electronics",
"thermal",
"mems",
"semiconductor"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Adnyeus__multiphysics-simulations",
"archive_path": "github/github/Adnyeus__multiphysics-simulations",
"posting_year": 2026
} | ri-2fdd0145a769 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": true,
"method": "example_run",
"oracles": 4,
"observed": 4,
"strong": 3,
"attempts": 2
} |
hymor | HYMOR is a specialized computational framework designed for the linear stability analysis of high-enthalpy hypersonic flows. It solves the compressible Navier-Stokes equations on structured curvilinear grids using a unique shock-fitting formulation that treats the bow shock as a sharp discontinuity via cubic-spline par... | [
"Implement a 2D and 3D-axisymmetric compressible Navier-Stokes solver using a second-order finite-volume discretization scheme, writing base-flow fields to stdout.",
"Implement a shock-fitting mechanism using cubic-spline parametrization and Rankine-Hugoniot jump conditions, outputting shock position coordinates ... | {
"invocation": "cli",
"argv": "hymor <config_file>",
"stdin": "none",
"stdout": "simulation results, eigenvalues, growth factors, and receptivity optimization data",
"stderr": "convergence logs, solver warnings, and error messages",
"exit_codes": {
"0": "execution successful",
"1": "solver failure ... | numerical | {
"year": 2026,
"platform": "MATLAB / Julia",
"language": "MATLAB",
"constraints": [
"requires MATLAB R2023b or Julia 1.9",
"requires GPU for accelerated stability analysis modes",
"requires Optimization and Curve Fitting toolboxes for MATLAB version"
]
} | hard | 4,500 | [
"any general-purpose PDE-CFD framework",
"shock-fitting libraries",
"black-box stability solvers"
] | [
"fluiddynamics",
"hypersonic",
"stability-analysis",
"numerical-methods"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AdrianAA00__HYMOR",
"archive_path": "github/github/AdrianAA00__HYMOR",
"posting_year": 2026
} | ri-9a24fd2967f0 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: MATLAB",
"kind": "skipped"
} |
panache-archive | Panache-archive is a specialized tool designed to build and manage historical air quality and weather datasets for France. It processes massive datasets from CAMS European air quality reanalyses and ERA5 weather models to create compact Parquet files used for pollution episode detection. The primary challenge lies in h... | [
"Accepts --variable and --year as command-line arguments to trigger a CAMS build process",
"Downloads monthly CAMS data from ADS for the specified year using credentials found in ~/.cdsapirc",
"Filters downloaded CAMS data to include only the bounding box of {lat_min} to {lat_max}° N and {lon_min} to {lon_max}°... | {
"invocation": "cli",
"argv": "panache-archive build --variable [name] --year [YYYY] [--months N]",
"stdin": "none",
"stdout": "Progress reports and build status",
"stderr": "API connection errors or data validation warnings",
"exit_codes": {
"0": "Build successful",
"1": "Build failed or authentic... | numerical | {
"year": 2026,
"platform": "multi-platform",
"language": "Python 3.12",
"constraints": [
"requires significant disk space for 0.8GB monthly downloads",
"requires network access to ADS and AWS S3",
"memory-intensive processing of large NetCDF grids"
]
} | hard | 2,400 | [
"panache-archive",
"cfd-framework",
"black-box-pde-solvers"
] | [
"meteorology",
"air-quality",
"parquet",
"geospatial"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AdrienJKTH__panache-archive",
"archive_path": "github/github/AdrienJKTH__panache-archive",
"posting_year": 2026
} | ri-10cb7ba64b7d | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
steel-solidification | This program implements a 1D finite-difference numerical simulation of the solidification process for 165XCrMoW high-alloy tool steel. It utilizes the apparent heat capacity method to model phase changes, specifically accounting for latent heat release during crystallization and the transition between liquidus and soli... | [
"Initialize a 1D spatial domain of # length mm divided into # nodes discrete nodes",
"Set the initial temperature of all nodes to the liquidus temperature of # liquidus_temp K",
"Apply a symmetry boundary condition (zero flux) at the center line (x=L)",
"Solve the heat conduction equation using an implicit Cr... | {
"invocation": "cli",
"argv": "steel-sim --config params.json",
"stdin": "none",
"stdout": "Simulation progress and path to generated result files",
"stderr": "Errors regarding parameter validation or numerical instability",
"exit_codes": {
"0": "Simulation completed successfully",
"1": "Numerical ... | numerical | {
"year": 2026,
"platform": "Generic OS",
"language": "Python (Jupyter)",
"constraints": [
"memory limits for large spatial temporal grids",
"requires high-precision linear algebra operations"
]
} | medium | 1,200 | [
"OpenFOAM",
"FEniCS",
"MATLAB",
"black-box thermal solvers"
] | [
"metallurgy",
"thermal-transfer",
"finite-difference",
"phase-change"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Afnnnan__steel-ingot-solidification-simulation",
"archive_path": "github/github/Afnnnan__steel-ingot-solidification-simulation",
"posting_year": 2026
} | ri-edccde426fc5 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
heat-transfer-sim | This program implements solvers for two distinct thermal engineering problems using numerical methods in MATLAB environment. The first component addresses steady-state heat conduction in a 1D plate with internal heat generation, involving complex boundary conditions where one side experiences both convection and radiat... | [
"Solve the steady-state 1D heat equation using the Finite Difference Method for a plate with an internal heat generation term",
"Apply combined convection and radiation boundary conditions at the left edge of the steady-state plate using parameters for h, epsilon, and T_amb",
"Apply an insulated (zero-flux) bou... | {
"invocation": "cli",
"argv": "heat-transfer-sim [mode: steady| transient] [params]",
"stdin": "none",
"stdout": "Status messages and paths to generated files",
"stderr": "Error messages or convergence failure warnings",
"exit_codes": {
"0": "Success",
"1": "Invalid parameters or numerical instabil... | numerical | {
"year": 2025,
"platform": "desktop",
"language": "MATLAB",
"constraints": [
"Requires MATLAB toolbox for plotting and Excel file generation",
"Explicit scheme must satisfy stability criteria (CFL condition) "
]
} | medium | 1,200 | [
"black-box CFD solvers",
"existing heat-transfer-specific frameworks",
"automated PDE solver libraries"
] | [
"heat-transfer",
"numerical-methods",
"finite-difference",
"simulation"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AhmedBsher__Heat_Transfer_Project",
"archive_path": "github/github/AhmedBsher__Heat_Transfer_Project",
"posting_year": 2025
} | ri-cda92466c09f | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: MATLAB",
"kind": "skipped"
} |
ductedfanlib | DuctedFanLib is a Python library designed for the parametric design and aerodynamic analysis of ducted fans and open rotors. It allows engineers and researchers to model propulsion systems by integrating Blade Element Momentum Theory (BEMT) to predict performance metrics. The tool handles complex geometries through lin... | [
"Generate NACA 4-digit airfoil coordinates from a string identifier (e.g., '4412')",
"Validate that a ducted fan assembly ensures the rotor radius fits within the duct internal radius",
"Perform an axial BEMT analysis for an open rotor calculating thrust and torque across a velocity range",
"Calculate the Fig... | {
"invocation": "library",
"argv": "",
"stdin": "none",
"stdout": "summary dataframes and printed metrics",
"stderr": "error messages for geometric validation failures",
"exit_codes": {
"0": "successful execution",
"1": "geometric validation or convergence failure"
},
"files": []
} | numerical | {
"year": 2025,
"platform": "generic",
"language": "Python",
"constraints": [
"requires numpy, scipy, pandas, neuralfoil, and aerosandbox",
"requires a Fortran compiler for XFoil-based methods"
]
} | hard | 2,800 | [
"black-box CFD solvers",
"existing BEMT frameworks",
"ductedfanlib"
] | [
"aerodynamics",
"bemt",
"propulsion",
"parametric design"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AhmedMoustafaa__ductedfanlib",
"archive_path": "github/github/AhmedMoustafaa__ductedfanlib",
"posting_year": 2025
} | ri-e7bca092cd74 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
pinch-analyzer | This tool facilitates the thermal design of heat exchanger networks (HEN) using the classical Pinch Analysis method. In chemical engineering, optimizing efficiency requires balancing the trade-off between capital costs (heat exchanger area) and operating costs (utility energy requirements). The program identifies the o... | [
"Read an input Excel file containing stream IDs, supply temperatures, target temperatures, and heat loads in kW.",
"Accept a value for the overall heat transfer coefficient (U) in W/m2.K via command-line argument.",
"Calculate the heat capacity flow rate (FCp) for each stream based on heat load and temperature ... | {
"invocation": "cli",
"argv": "pinch-analyzer <input.xlsx> <U_coefficient> <dTmin>",
"stdin": "none",
"stdout": "Summary of pinch point location and utility requirements",
"stderr": "Error messages regarding file parsing or mathematical convergence",
"exit_codes": {
"0": "Success",
"1": "Input file... | numerical | {
"year": 2020,
"platform": "Universal",
"language": "Python",
"constraints": [
"requires pandas and openpyxl for Excel processing",
"memory limits for large stream count matrices"
]
} | medium | 1,200 | [
"scipy.optimize",
"commercial-hen-solvers",
"pinch-specific-libraries"
] | [
"thermal",
"pinch-analysis",
"chemical-engineering",
"energy-optimization"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Ak724904__heat-exchanger-network-design-pinch-area-targeting",
"archive_path": "github/github/Ak724904__heat-exchanger-network-design-pinch-area-targeting",
"posting_year": 2020
} | ri-a803a40d20c2 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
heat-to-cold | This program is a physics-based simulation and optimization tool for solar-powered mini cold-storage systems designed for remote regions. It models the complex coupling between solar PV energy generation, battery storage, biochar-enhanced organic phase change material (PCM) thermal storage, and refrigeration cycles. Th... | [
"Parses a PVGIS ERA5 JSON weather file to extract hourly solar radiation and temperature data for a full year",
"Calculates the room envelope heat gain based on provided transmission UA and infiltration coefficients against ambient outdoor temperatures",
"Simulates the thermal state of organic PCM using an enth... | {
"invocation": "cli",
"argv": "heat-to-cold --config config.json --output results/",
"stdin": "none",
"stdout": "Optimization summary, Pareto front statistics, and simulation status",
"stderr": "Warnings regarding convergence issues or physical constraint violations",
"exit_codes": {
"0": "Execution su... | numerical | {
"year": 2026,
"platform": "cross-platform",
"language": "Python 3.x",
"constraints": [
"requires memory for high-resolution transient simulation of 8760 steps",
"requires numerical-heavy libraries for matrix operations"
]
} | hard | 2,400 | [
"scipy.optimize",
"pyoo",
"deap",
"black-box refrigeration-solvers"
] | [
"cfd",
"thermal",
"solar-energy",
"optimization",
"simulation"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Akshath78__Heat-to-Cold",
"archive_path": "github/github/Akshath78__Heat-to-Cold",
"posting_year": 2026
} | ri-07f6798e094a | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
airfoil-noise-pred | This program implements a machine learning pipeline to predict the Scaled Sound Pressure Level (SPL) of airfoils based on aerodynamic properties. In aeroacoustics, predicting noise levels through traditional Computational Fluid Dynamics is computationally expensive, creating a need for fast machine learning approximati... | [
"Parses a CSV file containing the NASA Airfoil Self-Noise dataset with specific required columns",
"Trains a Random Forest Regressor and outputs its specific MSE and R² score",
"Trains a Polynomial Regression model with a degree of 4 to capture non-linear effects",
"Computes the Mean Squared Error (MSE) and R... | {
"invocation": "cli",
"argv": "airfoil-noise-pred <input_csv> <output_json>",
"stdin": "none",
"stdout": "model performance comparison table and predicted noise level values",
"stderr": "error messages regarding file access or convergence failure",
"exit_codes": {
"0": "success",
"1": "file not fou... | ai | {
"year": 2025,
"platform": "Generic",
"language": "Python/Jupyter",
"constraints": [
"requires memory for large random forest trees",
"requires numerical algebra libraries for matrix backpropagation"
]
} | medium | 1,200 | [
"scikit-learn",
"tensorflow",
"pytorch",
"keras-frameworks"
] | [
"aeroacoustics",
"machine-learning",
"regression",
"noise-prediction"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Akurt-star__Airfoil-Self-Noise-Prediction-with-Machine-Learning",
"archive_path": "github/github/Akurt-star__Airfoil-Self-Noise-Prediction-with-Machine-Learning",
"posting_year": ... | ri-24e14a11b09c | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
inter-labs | This program implements a suite of geophysical numerical models designed to replace manual Excel laboratory simulations with reproducible Python code. It covers four distinct domains: Ekman boundary layer dynamics for wind-driven ocean currents, a three-layer coupled ocean-atmosphere box model for heat exchange, 1D lon... | [
"Calculate the Ekman boundary layer profile and hodograph given geostrophic wind speed, density contrasts, and boundary layer depths, saving results to figures/01_ekman_profiles.png and figures/01_ekman_hodograph.png",
"Verify continuity at the atmosphere-ocean interface for the Ekman model with a specified toler... | {
"invocation": "cli",
"argv": "python -m oahm [model_name] [input_files] [output_params]",
"stdin": "none",
"stdout": "convergence metrics, skill scores, and model-summary statistics",
"stderr": "numerical stability warnings and optimization logs",
"exit_codes": {
"0": "success",
"1": "numerical in... | numerical | {
"year": 2026,
"platform": "multi-platform",
"language": "Python 3.10+",
"constraints": [
"requires numpy and pandas for vectorized operations",
"requires openpyxl for modern Excel parsing",
"requires scikit-learn for gradient boosting and optimization"
]
} | hard | 2,800 | [
"Excel",
"black-box CFD solvers",
"high-level PDE-frameworks"
] | [
"oceanography",
"hydrology",
"geophysics",
"numerical-methods",
"machine-learning"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Al-St__inter-labs",
"archive_path": "github/github/Al-St__inter-labs",
"posting_year": 2026
} | ri-cb8fc8cf97f0 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "skipped: Jupyter Notebook",
"kind": "skipped"
} |
astrofluid | AstroFluid is a high-performance astrophysical simulation engine designed to model the evolution of self-gravitating gas clouds. It integrates Smoothed Particle Hydrodynamics (SPH) for gas dynamics with a Barnes–Hut octree solver to handle N-body gravity efficiently. The primary challenge lies in maintaining numerical ... | [
"Initialize a 3D particle cloud using Poisson disk sampling to ensure a uniform distribution without particle overlap for particle_count particles",
"Compute particle gravitational accelerations using the Barnes–Hut algorithm with a configurable theta opening angle",
"Calculate SPH density and pressure for each... | {
"invocation": "cli",
"argv": "./astrofluid [config_file]",
"stdin": "none",
"stdout": "simulation progress statistics and step counts",
"stderr": "numerical convergence warnings and error messages",
"exit_codes": {
"0": "simulation completed successfully",
"1": "configuration or parse error",
... | numerical | {
"year": 2026,
"platform": "multi-core CPU",
"language": "C++20",
"constraints": [
"C++20 compatible compiler",
"OpenMP for threading",
"SIMD vectorization memory alignment requirements",
"memory-intensive Structure of Arrays layout"
]
} | hard | 2,800 | [
"any-existing SPH frameworks",
"black-box N-body gravity solvers",
"Barnes-Hut library implementations"
] | [
"astrophysics",
"sph",
"barnes-hut",
"hpc",
"numerical-methods"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Alameen-datasci__AstroFluid",
"archive_path": "github/github/Alameen-datasci__AstroFluid",
"posting_year": 2026
} | ri-72d4e226e68f | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
bollinger-kerr-drive | The Bollinger-Kerr Drive is a theoretical framework designed to simulate metric-decoupled propulsion within Kerr spacetime. The primary objective is to negate relativistic time dilation by using high-frequency scalar perturbations to amplify frame-dragging effects in a rotating black hole. This program calculates the '... | [
"Calculate and print the coherence value C using the formula: C = exp(-0.08 / Q^0.09) * (Q/(Q + 1e9))^0.95 * f^0.75 * V^0.25",
"Accept Q (cavity quality factor), f (frequency in PHz), and V (vortex multiplier) as numerical inputs via CLI arguments",
"Print 'FTL threshold achieved' if C ≥ 2.5, otherwise print 'b... | {
"invocation": "cli",
"argv": "bkd_virtual_horizon_simulation.py --Q [value] --f [value] --V [value]",
"stdin": "none",
"stdout": "Coherence value, FTL status, estimated aging, and file paths for plots",
"stderr": "errors regarding invalid physical parameters",
"exit_codes": {
"0": "simulation complete... | numerical | {
"year": 2025,
"platform": "generic",
"language": "Python 3.8+",
"constraints": [
"requires numpy for numerical operations",
"requires scipy for scientific functions",
"requires matplotlib for visualization generation"
]
} | medium | 800 | [
"black-box PDE solvers",
"general-purpose CFD frameworks",
"kerr-metric-libraries"
] | [
"relativity",
"black holes",
"propulsion",
"numerical-simulation"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Albuslux1__Bollinger-Kerr-Drive",
"archive_path": "github/github/Albuslux1__Bollinger-Kerr-Drive",
"posting_year": 2025
} | ri-ab1e7ce80e8c | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
satellite-s5p | This toolkit is designed to automate the retrieval, processing, and visualization of atmospheric data from multiple satellite sources including Sentinel-5P (TROPOMI), MODIS, GEMS, and ERA5. It solves the complex problem of heterogeneous satellite datasets which vary significantly in spatial resolution, temporal frequen... | [
"Read configuration from a .env file including API keys for Copernicus, NASA, GEMS, and CDS, and a base storage directory",
"Implement a Sentinel-5P hub that fetches NO2-related products based on a date range and a geographic bounding box",
"Support S3-based bulk downloads for Sentinel-5P to bypass standard HTT... | {
"invocation": "cli",
"argv": "python -m scripts.l3_regrid_year --source [src] --product [prod] --year [year] [--dry-run]",
"stdin": "none",
"stdout": "Progress bars, coverage statistics, and file paths for output",
"stderr": "Logs, error messages, and rate limit warnings",
"exit_codes": {
"0": "succes... | numerical | {
"year": 2023,
"platform": "multi-platform",
"language": "Python 3.12",
"constraints": [
"requires disk space for terabyte-scale archives",
"requires network access for API calls",
"pyhdf compatibility limited to Python < 3.14"
]
} | hard | 2,500 | [
"existing-cfd-framework",
"black-box-pde-solver",
"the-package-its"
] | [
"satellite",
"environmental science",
"regrid",
"netcdf"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Alex870521__Satellite_S5P",
"archive_path": "github/github/Alex870521__Satellite_S5P",
"posting_year": 2023
} | ri-31e4689c4548 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
parabolic-solver | This numerical library provides a framework for solving parabolic partial differential equations, specifically the heat/diffusion equation and the time-dependent Schrödinger equation in 1D and 2D domains. It utilizes finite difference methods to approximate solutions over time, implementing the implicit Crank-Nicolson ... | [
"Instantiate a Diffusion_1d simulation class with domain length L, total time T, spatial grid points Nx, and time steps Nt.",
"Implement a Crank-Nicolson solver for 1D diffusion that accepts an initial distribution function, Dirichlet boundary conditions, and a diffusion coefficient.",
"Implement a Crank-Nicols... | {
"invocation": "library",
"argv": "",
"stdin": "none",
"stdout": "none",
"stderr": "none",
"exit_codes": {
"0": "success",
"1": "parameter error or convergence failure"
},
"files": []
} | numerical | {
"year": 2024,
"platform": "generic",
"language": "Python",
"constraints": [
"requires numpy for linear algebra and array operations",
"requires matplotlib for result visualization"
]
} | medium | 1,200 | [
"scipy.sparse.linalg",
"fenics",
"any-pde-solver-libraries"
] | [
"pde",
"finite-difference",
"diffusion",
"quantum-physics",
"crank-nicolsonson"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "AlexStratou__Solving-parabolic-PDEs-with-finite-differences-methods",
"archive_path": "github/github/AlexStratou__Solving-parabolic-PDEs-with-finite-differences-methods",
"posting... | ri-1b9a2a756d77 | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 2
} |
roms_generator | ROMS.jl is a specialized Julia framework designed to automate the configuration and data preparation for the Regional Ocean Modeling System (ROMS). Setting up oceanographic simulations requires meticulous pre-processing of heterogeneous geophysical datasets, including bathymetry, initial conditions, and atmospheric for... | [
"Generate a valid ROMS configuration file from a Julia-based specification defining grid dimensions and physics",
"Import bathymetry data from GEBCO NetCDF files and interpolate to the specified curvilinear grid",
"Process initial and boundary conditions from CMEMS or HYCOM GOFS sources into ROMS-compatible arr... | {
"invocation": "library",
"argv": "",
"stdin": "none",
"stdout": "status updates and generated file paths",
"stderr": "error messages regarding data missing or coordinate inconsistencies",
"exit_codes": {
"0": "success in file generation",
"1": "data validation failure or configuration error"
},
... | numerical | {
"year": 2020,
"platform": "generic",
"language": "Julia",
"constraints": [
"requires Julia 1.9 or later",
"memory availability for large-scale global NetCDF datasets",
"network access for API-based data"
]
} | hard | 2,400 | [
"ROMS core solver binary",
"high-level CFD frameworks",
"black-box PDE solver libraries"
] | [
"oceanography",
"netcdf",
"geophysical",
"pre-processing",
"julia"
] | {
"source": "mined",
"msg_id": null,
"group": "github",
"date": null,
"signal": "announce",
"expansion": null,
"package": "Alexander-Barth__ROMS.jl",
"archive_path": "github/github/Alexander-Barth__ROMS.jl",
"posting_year": 2020
} | ri-664ebd96ba1b | {
"status": "ok",
"failed_stage": null,
"failures": []
} | {
"verified": false,
"method": "none",
"reason": "the shipped example did not build or run",
"kind": "sandbox",
"run_status": "run-failed",
"attempts": 3
} |
End of preview. Expand in Data Studio
cfd-2k-v0
Programming-task specifications for computational fluid dynamics and adjacent scientific software. Each record is derived from one real program and its own documentation: a task description, 6-16 atomic testable requirements, an interface (argv, stdin/stdout, files, exit codes), era constraints, forbidden shortcuts, and provenance back to the source program.
This is the CFD counterpart of gdiamos/classic-eda
and uses the same record schema (record.schema.json), extended with pipeline and
verification blocks.
Files
| config | file | records | contents |
|---|---|---|---|
| default | data/train.jsonl |
2,133 | records with pipeline.status == "ok" |
| all_candidates | data/all_candidates.jsonl |
17,029 | every candidate considered, including triage drops and pending ones; nothing was deleted |
How it was made
All model calls went to a self-hosted nvidia/diffusiongemma-26B-A4B-it-NVFP4 (vLLM), with
up to 10 attempts per call and the failure fed back on each retry.
- Sources: GitHub repositories found by searching 17 CFD tool groups (aero, thermal, FEniCS, turbulence/reacting flow, gmsh, solvers, coastal/atmospheric, ...), OpenFOAM utilities, PDAS legacy codes, PyPI packages, benchmark suites, and textbook codes.
- Triage (strict): the model answers factual questions about each program; code decides whether it is a self-contained, file-in/file-out program. 5,190 of 17,029 kept.
- Large programs split into self-contained units.
- Selection: at most 2 records per problem cluster and 2 per parent program.
- Extraction into the schema; accepted by a validator and mechanical checks.
- FORMAT.md written for the inputs/outputs; each requirement audited for testability.
- Repair of flagged requirements and re-audit; untestable requirements dropped while at least 6 remain.
- Verification (flag only): the program's shipped example is built and run in an offline podman sandbox and oracle values are regenerated from the captured output.
pipeline.status == "ok" means the record passed steps 2-7. It does not mean verified.
Verification
| records | |
|---|---|
verified (verification.verified == true) |
400 (19%) |
example did not build or run (kind: sandbox) |
1,306 |
no runnable example (kind: skipped) |
368 |
| ran, but no oracle value observed | 53 |
| other | 6 |
Most sandbox failures are the programs' own: compile errors, Python dependencies the recipe did not list, missing input files, timeouts. The sandbox has no network during the run.
Composition
- Era (by
era.year): 2020s 1,821; 2010s 248; 2000s 2; 1990s 52; before 1990 10. Unlike classic-eda this set is not era-balanced; it is dominated by modern GitHub code. - Language: Python 1,275; MATLAB 183; C++ 183 (+40 C++17); Fortran 174; Julia 91; C 55; Rust 53.
- Difficulty: hard 1,155; medium 950; easy 28.
- Requirements per record: median 10.
Limitations
- Requirements were written and audited by the same model; the testability audit is self-graded. Only the 400 verified records have oracle values observed from running the original program.
- At most 2 records per problem cluster, but near-duplicates across clusters may remain.
- Records describe the source program; they do not include its code. Licenses are those of
each source repository (see
provenance.package/provenance.archive_path); check them before redistributing derived code. - v0: a human review of a 20-record sample is pending.
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