| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - physics-informed-neural-networks |
| - pinn |
| - pde |
| - benchmark |
| - evaluation |
| - routing |
| - causal-loss |
| - fourier-neural-operator |
| - deeponet |
| - scientific-machine-learning |
| pretty_name: PINNBench |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - other |
| configs: |
| - config_name: routing_evaluation |
| data_files: results_paper_combined/routing_evaluation/*.json |
| - config_name: stage_ablation |
| data_files: results_paper_combined/stage_ablation/*.json |
| - config_name: meta_router |
| data_files: results_paper_combined/meta_router/*.json |
| - config_name: regret_bound_validation |
| data_files: results_paper_combined/routing_evaluation/regret_bound_validation.json |
| - config_name: adaptive_epsilon |
| data_files: results_paper_combined/adaptive_epsilon/*.json |
| - config_name: external_probes |
| data_files: results_paper_combined/{hypino,pinnacle,wang}*/*.json |
| --- |
| |
| # PINNBench |
|
|
| A controlled benchmark for evaluating training-policy selection in hybrid Physics-Informed Neural Network (PINN) and neural-operator solvers. PINNBench accompanies the paper *"PINNBench: A Benchmark and Evaluation Study of Training Policy Selection in Hybrid PINN-Operator Solvers"* (NeurIPS 2026, Evaluations and Datasets Track). Code: <https://github.com/suanlab/PINNBench>. |
|
|
| ## What is in this repository |
|
|
| This Hugging Face dataset hosts the **benchmark protocol artifacts** + **logged results** of 1,539 controlled training runs on 13 PDE configurations spanning 8 equation families. The dataset is **not raw simulation data** (PDE reference solutions are analytical and regenerated at runtime); it is the **decision-relevant evaluation record** that supports the paper's headline claims. |
|
|
| ``` |
| results_paper_combined/ |
| ├── routing_evaluation/ |
| │ ├── results.json # 13-PDE leave-one-out CV (450 routing decisions × 4 selectors) |
| │ ├── holdout.json # 3-PDE held-out generalization |
| │ ├── regret_bound_validation.json # 33/52 PDE × probe-window pairs |
| │ └── scaling_ablation.json # routing accuracy vs. PDE count |
| ├── meta_router/ |
| │ └── results.json # PDE-aware router (84.6% with 5+7 features) |
| ├── stage_ablation/ |
| │ └── *_ablation.json # 9 PDEs × 2×2 (Stage1, Stage3) factorial × 10 seeds |
| ├── adaptive_epsilon/ |
| │ └── results.json # 4 PDEs × 4 conditions × 5 seeds (causal-weight collapse) |
| ├── wang_comparison/ |
| │ └── *_results.json # Wang-style single-stage causal probe (4 PDEs) |
| ├── hypino_*/ # HyPINO native, adapter, target-PINN adaptation probes |
| ├── pinnacle_subset_5task/ |
| │ └── results.json # PINNacle 5-task executable subset |
| ├── statistical_tests.json # BH-FDR corrected, 27 tests |
| │ # --- added in v1.1.0 (camera-ready) --- |
| ├── noise_sparsity_sweep*/ # 6 PDEs x 3 noise x 4 data sizes x 2 conditions x 5 seeds (720 runs) |
| ├── heat3d_probe/ # d=3 pretraining contrast, 10 seeds |
| ├── multiscale2d_probe/ # MultiscaleHeat2D contrast + per-mode projection, 10 seeds |
| ├── multiscale2d_policy_suite/ # full 5-policy library on MultiscaleHeat2D, 50 runs |
| ├── dissociation_transfer/ # accuracy-regret dissociation on tabular classifier selection |
| └── k20_gap_check.json # regret-bound check at probe window k=20 |
| ``` |
|
|
| ## How to load |
|
|
| The recommended access pattern is direct JSON read; result schemas are documented in `MANIFEST.md`. |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import json |
| |
| routing_path = hf_hub_download( |
| "suanlab/PINNBench", |
| "results_paper_combined/routing_evaluation/results.json", |
| repo_type="dataset", |
| ) |
| with open(routing_path) as f: |
| routing = json.load(f) |
| ``` |
|
|
| `datasets`-library config aliases (`routing_evaluation`, `stage_ablation`, `meta_router`, `regret_bound_validation`, `adaptive_epsilon`, `external_probes`) are declared in the YAML header above for convenience. |
|
|
| ## Verification |
|
|
| Every headline claim in the paper is recomputed from these JSONs by the script `scripts/verify_claims.py` in the source repository. Reproduced PASS results: |
|
|
| | Claim | Source artifact | Computed | Paper | |
| |---|---|---|---| |
| | Total runs | union | 1,510 enumerable + 29 probes | 1,539 | |
| | Nested PDE-aware routing accuracy | `meta_router/results.json` | 84.62% (11/13) | 84.6% | |
| | Full RF+PDE routing | `meta_router/results.json` | 61.54% (8/13) | 61.5% | |
| | Stage-2 compute savings | analytical | 60.0% (K=3, Eₚ=50, E₂=500) | 60% | |
| | Diagnostic regret bound | `regret_bound_validation.json` | 33/52 overall, 33/39 if k∈{10,50,100} | 33/52, 33/39 | |
| | Accuracy–regret dissociation | `routing_evaluation/results.json` folds + `results/paper_a/*` runs | 0.0850 vs 0.0011 (77.6× unrounded) | 77× | |
| | Family-macro accuracy (LOPO-F) | `routing_evaluation/results.json` | 81.2% physics-loss-final (6/8) | 81.2% | |
| | KdV1D causal effect (PDE residual) | `rq1e_kdv_10seed_causal/results.json` | 0.0096 / 0.0105 | 0.010 / 0.011 | |
| | Adaptive-ε min causal weight (AdvDiff1D) | `adaptive_epsilon/results.json` | 0.78 (fixed, pretrained) → 0.98 (adaptive, no pretraining) | same | |
|
|
| ## Reproducibility caveats |
|
|
| - All training results were computed at the **final epoch** of each stage (no best-checkpoint selection, no validation set). |
| - Evaluation points were regenerated per seed via `torch.rand` rather than loaded from a fixed grid; per-seed metric variance therefore includes evaluation-point sampling variance. |
| - The `rq1e_kdv_10seed_causal/results.json` summary aggregate has a `nan-mean` bug for some fields; per-seed means are recomputed by the verification script. |
|
|
| ## Croissant metadata |
|
|
| `croissant.json` (Croissant 1.1, version 1.1.0) carries 13 `rai:` fields. `mlcroissant validate --jsonld croissant.json` (mlcroissant 1.1.0) reports 0 errors and 1 warning: the `@context` includes `examples`, `equivalentProperty` and `samplingRate`, which are Croissant 1.1 keys absent from the 1.0 reference context. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{lee2026pinnbench, |
| title = {{PINNBench}: A Benchmark and Evaluation Study of Training Policy Selection in Hybrid {PINN}-Operator Solvers}, |
| author = {Lee, Suan and Kim, Namhyeon and Jin, Dongmin}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache-2.0 for code and benchmark artifacts. PDE reference solutions are analytical and original to this work; no third-party data is redistributed. |
|
|
| ## Contact |
|
|
| Issues and questions: <https://github.com/suanlab/PINNBench/issues>. |
|
|
