Publish 256 verified AgentFEM heat-conduction cases

CODE_LICENSE

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LICENSE

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+Dataset: Creative Commons Attribution 4.0 International (CC BY 4.0).
+https://creativecommons.org/licenses/by/4.0/
+Copyright 2026 Haoming Luo. Attribute this dataset and AgentFEM.
+Python examples: Apache-2.0; see CODE_LICENSE.

README.md

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 ---
 license: cc-by-4.0
+language:
+- en
+tags:
+- agentfem
+- finite-element-method
+- scientific-machine-learning
+- physics-ai
+- simulation
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: validation.jsonl
+  - split: test
+    path: test.jsonl
 ---
+# AgentFEM · Multi-source heat conduction
+
+Two smooth volumetric heat sources in a rectangular conducting plate. Learn how source placement, spread, intensity, geometry and conductivity shape the temperature field.
+
+![AgentFEM finite-element examples](preview.png)
+
+**256 independently solved parameter sets · full meshes and fields · 192 / 32 / 32 split · CC BY 4.0**
+
+Built with [AgentFEM](https://github.com/haoming-luo/agentfem). A small, reproducible engineering dataset for surrogate learning, field prediction and numerical-method experiments.
+
+## Physical problem
+
+Steady isotropic conduction: -div(k grad(T)) = q. All four edges are held at 300 K. The two Gaussian sources are uniform through the unit out-of-plane thickness. No air flow, contact resistance, radiation, or convection is solved.
+
+Linear triangular temperature elements; 48 by 32 structured subdivisions (3,072 triangles).
+
+An idealized heat-spreading plate with isothermal cooled edges, not a complete electronic cooling system. Positive Gaussian heat sources may overlap. Sampling confines source 1 to the lower-left and source 2 to the upper-right part of the plate.
+
+## What is included
+
+- `train.h5`, `validation.h5`, `test.h5`: one group per sample ID, geometry, connectivity, point fields and cell fields. Gzip-compressed without floating-point downcasting.
+- HDF5 `inputs/`: boundary-node masks and, for heat, the prescribed Gaussian source evaluated at mesh nodes. These are model inputs, not predicted fields or integrated nodal loads.
+- Corresponding JSONL files: browsable parameters and engineering observables; they point to HDF5 groups, not serialized full fields.
+- `quality.json`: checks, dependency versions, convergence evidence.
+- `baselines.json`, `baseline.py`: held-out scalar mean/nearest-neighbor baselines with train-only normalization; no neural-model claims.
+- `generate.py`, `quality.py`, `load_example.py`: generation, audit and minimal loading workflows.
+
+SI units: coordinates and U in m; stress S and MISES in Pa; strain E dimensionless; temperature in K. `coordinates` are undeformed reference nodes. Connectivity follows the AgentFEM XDMF export order: Triangle (heat), Triangle_6 (notch), or Quadrilateral_9 (cylinder). Cell stress is projected and unsmoothed. See generated XDMF for native export semantics.
+
+Tensor components are flattened row-major: 2×2 for plane stress (x,y), 3×3 for axisymmetry (r,theta,z). Displacement U uses padded three-component storage; active components are (x,y) for the plate and (r,z) for the cylinder. The HDF5 files contain mesh connectivity, not an assumed common image grid.
+
+Parameters, in generator order: `length_m, height_m, conductivity_w_m_k, source1_x_fraction, source1_y_fraction, source2_x_fraction, source2_y_fraction, source1_sigma_m, source2_sigma_m, source1_peak_w_m3, source2_peak_w_m3`.
+
+Observed sample ranges (exact values are in JSONL; intended sampling bounds are in `generate.py`):
+
+| Parameter | Minimum | Maximum |
+|---|---:|---:|
+| length_m | 0.0801395 | 0.159853 |
+| height_m | 0.0501502 | 0.0998562 |
+| conductivity_w_m_k | 15.6592 | 199.899 |
+| source1_x_fraction | 0.150413 | 0.449133 |
+| source1_y_fraction | 0.151047 | 0.449245 |
+| source2_x_fraction | 0.550528 | 0.849329 |
+| source2_y_fraction | 0.550023 | 0.849833 |
+| source1_sigma_m | 0.00402783 | 0.0119955 |
+| source2_sigma_m | 0.00402299 | 0.0119821 |
+| source1_peak_w_m3 | 1.01861e+06 | 7.97658e+06 |
+| source2_peak_w_m3 | 1.0042e+06 | 7.97922e+06 |
+
+## Quality evidence
+
+Eight mesh-refinement audits: maximum relative change in peak_temperature_rise_k = 0.44%. This is an observable convergence check, not a bound on full-field error.
+
+All published field arrays are finite; all parameter sets and geometry/source-layout parameter tuples are unique. Splits use one fixed random permutation after scrambled Sobol sampling (seed 20260923; split seed 20260924). All splits are **in-distribution**; no claim of unseen-family generalization. One row is one independently solved case, not a frame or a rescaled duplicate.
+
+Generic continuous material ranges are synthetic, not calibrated material grades. Dataset generation does not imply experimental validation. These are original parameterized examples, not certified NAFEMS benchmarks or copies of commercial solver datasets.
+
+## Load and reproduce
+
+```bash
+python load_example.py --repo HaomingLuo/AgentFEM-MultiSource-Heat-2D --split test
+# In the pinned AgentFEM/FEniCSx environment:
+python generate.py heat --count 256
+```
+
+Loading requires only h5py, numpy and huggingface_hub; FEniCSx is needed only to regenerate FEM results. Generation writes a local `generated/` directory and uses one MPI rank. The heat manufactured-solution check is available via `python generate.py heat --validate`.
+
+## Attribution
+
+Haoming Luo, *Multi-source heat conduction — AgentFEM parametric FEM dataset*, 2026. Please cite this repository URL and the commit revision used. Data: CC BY 4.0. Example code: Apache-2.0.

SHA256SUMS.json

@@ -0,0 +1,19 @@
+{
+  "train.jsonl": "5ec896d193e7522ea4c37cd9fafee6aaa90357a708c674895a3fa250933e05a9",
+  "preview.png": "f5c2f0ef0e97da37642ff705c4f356cf744b4689f19ccd17ad76d242a886b823",
+  "test.jsonl": "a6fe040b9a4c71c3dbb88f44535aa5d0b170423b7a6f9009e601f615dc33785d",
+  "LICENSE": "6c62c24ab27c3d599bc1944b2a45b71a0845283a07f8077db058082f93f6f3dd",
+  "environment.yml": "80cff57c525e2b76b28cb72679d1ff4f4f5db3ea9607222e272bb5bca08dd9d6",
+  "baseline.py": "0980f273c8b08cc51fa36cf447ecf4c4f35ff93c03786a21022d05f58fbe8635",
+  "generate.py": "f00a02ba3c21650bebe95a564576ff8ab9d1582a6a6ce2c437b63ded74285021",
+  "test.h5": "627341c89843e750a114aafefa528268fc7a9f6c36b0f227007ba13352942a6c",
+  "baselines.json": "cf0da6ef52109fa2ad1e02bc4259ef20e63c7bce5b06adf6a3c2200a535412de",
+  "quality.json": "648598051856ae9eb1819f546892947b2c52d9e8bc323f699d0fa026011ff452",
+  "README.md": "3e5605c92bb670b1d4296143eb8de8eb2354a69b89808907bd7d750281aadfcd",
+  "train.h5": "46ab81886c624d7018cb46ef89b4b49138dd5dcedb7f11b3add5c2da68b3825a",
+  "validation.jsonl": "9baa9c0aad559bc1bec911932f632c6ebc792a340c9d5d99d4a6b7e9b1ab5d1d",
+  "CODE_LICENSE": "4a8e89fe65965e0b5ab34a6a1b8faa412666e426ce92500dba6af0c8f3e0ca0b",
+  "validation.h5": "005a5d42064eeecd62ba959bc6a9929f22c02eb1fc968e449cf06e8e204dd79e",
+  "quality.py": "734b8483f691b3c686010e9e524f1f95e860f4f6041883dd617d458349c8bdc5",
+  "load_example.py": "80dec47885be778a477c6eec752293bbb727949dbf5b98e3651bbe9d6f8226f7"
+}

baseline.py

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+"""Scalar baselines on the released splits. Run inside the downloaded repo."""
+import json
+from pathlib import Path
+import numpy as np
+
+if __name__=="__main__":
+    root=Path(__file__).resolve().parent
+    info=json.loads((root/"baselines.json").read_text()); target=info["target"]
+    train=[json.loads(s) for s in (root/"train.jsonl").read_text().splitlines()]
+    test=[json.loads(s) for s in (root/"test.jsonl").read_text().splitlines()]
+    exclude={"id","split","hdf5","group","nodes","cells","parameter_sha256","relative_nodal_l2_error",target}
+    names=[k for k in train[0] if k not in exclude]
+    X=np.array([[r[k] for k in names] for r in train]); Xt=np.array([[r[k] for k in names] for r in test])
+    y=np.array([r[target] for r in train]); yt=np.array([r[target] for r in test])
+    mu=X.mean(0); s=X.std(0); X=(X-mu)/s; Xt=(Xt-mu)/s
+    idx=np.argmin(((Xt[:,None,:]-X[None,:,:])**2).sum(2),axis=1)
+    for name,pred in [("train_mean",np.full_like(yt,y.mean())),("nearest_neighbor",y[idx])]:
+        print(name,"relative L2",np.linalg.norm(pred-yt)/np.linalg.norm(yt),"MAE",np.mean(abs(pred-yt)))

baselines.json

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+{
+  "target": "peak_temperature_rise_k",
+  "train_n": 192,
+  "test_n": 32,
+  "models": {
+    "train_mean": {
+      "test_relative_l2": 0.6931062834630907,
+      "test_mae": 4.829315904783906,
+      "unit": "K"
+    },
+    "nearest_neighbor": {
+      "test_relative_l2": 0.7071034966232062,
+      "test_mae": 4.379815514622678,
+      "unit": "K"
+    }
+  }
+}

environment.yml

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+name: agentfem-data
+channels:
+  - conda-forge
+dependencies:
+  - python=3.11
+  - fenics-dolfinx=0.11.0
+  - gmsh=4.15.2
+  - h5py
+  - scipy
+  - matplotlib
+  - pip
+  - pip:
+      - agentfem==0.3.3
+      - huggingface_hub

generate.py

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+"""Reproducible AgentFEM small-problem dataset factory. SI units; serial MPI."""
+import argparse, hashlib, json, time
+from pathlib import Path
+import numpy as np
+import ufl
+from mpi4py import MPI
+from scipy.stats import qmc
+from agentfem import mesh, models, fields, studies, constitutive, constraints
+from agentfem.solvers import LinearSolverOptions
+
+ROOT = Path(__file__).resolve().parent
+SOLVER = LinearSolverOptions(ksp_type="preonly", pc_type="lu")
+
+def run(kind, p, folder, refinement=1, analytical=False):
+    folder.mkdir(parents=True, exist_ok=True)
+    started = time.monotonic()
+    if kind == "heat":
+        L,H,k,x1,y1,x2,y2,s1,s2,q1,q2 = p
+        domain = mesh.rectangle((0,0),(L,H),(48*refinement,32*refinement),cell_type="triangle")
+        model = models.create(study=studies.steady_heat_transfer(dimension=2),mesh=domain)
+        f = model.field(fields.temperature(domain,value=300.))
+        model.material(constitutive.thermoelastic(young=200e9,poisson=.3,density=7800,
+            thermal_expansion=12e-6,conductivity=k,specific_heat=500,reference_temperature=300))
+        boundary = mesh.boundary(domain,lambda x: np.isclose(x[0],0)|np.isclose(x[0],L)|np.isclose(x[1],0)|np.isclose(x[1],H),tag=1)
+        model.fix(f,on=boundary,value=300.)
+        x=ufl.SpatialCoordinate(domain)
+        source = (k*((np.pi/L)**2+(np.pi/H)**2)*10*ufl.sin(np.pi*x[0]/L)*ufl.sin(np.pi*x[1]/H)
+            if analytical else q1*ufl.exp(-((x[0]-x1*L)**2+(x[1]-y1*H)**2)/(2*s1*s1))+q2*ufl.exp(-((x[0]-x2*L)**2+(x[1]-y2*H)**2)/(2*s2*s2)))
+        model.heat_source(source)
+    else:
+        if kind == "cylinder":
+            a,b,E,nu,pressure = p
+            domain=mesh.rectangle((a,0),(b,.02),(24*refinement,2*refinement),cell_type="quadrilateral")
+            study=studies.static_solid(dimension=2,assumption="axisymmetric")
+        else:
+            import gmsh
+            L,H,r,E,nu,traction=p
+            if not gmsh.isInitialized(): gmsh.initialize()
+            gmsh.clear(); gmsh.option.setNumber("General.Terminal",0)
+            gmsh.model.add("double_edge_notch")
+            plate=gmsh.model.occ.addRectangle(0,0,0,L,H)
+            c1=gmsh.model.occ.addDisk(L/2,0,0,r,r)
+            c2=gmsh.model.occ.addDisk(L/2,H,0,r,r)
+            surfaces,_=gmsh.model.occ.cut([(2,plate)],[(2,c1),(2,c2)])
+            gmsh.model.occ.synchronize()
+            gmsh.model.addPhysicalGroup(2,[t for d,t in surfaces],1)
+            gmsh.option.setNumber("Mesh.MeshSizeMin",H/(40*refinement))
+            gmsh.option.setNumber("Mesh.MeshSizeMax",H/(20*refinement))
+            gmsh.model.mesh.generate(2)
+            domain=mesh.import_gmsh_model(gmsh.model,gdim=2).domain
+            study=studies.static_solid(dimension=2,assumption="plane_stress")
+        model=models.create(study=study,mesh=domain)
+        f=model.field(fields.displacement(domain,degree=2))
+        model.material(constitutive.isotropic_elastic(young=E,poisson=nu,density=7800))
+        if kind=="cylinder":
+            model.constraint(constraints.axisymmetric_plane_strain(f))
+            boundary=mesh.face(domain,axis="x",value=a,tag=1)
+            model.pressure(pressure,on=boundary)
+        else:
+            left=mesh.boundary(domain,lambda x:np.isclose(x[0],0),tag=1)
+            right=mesh.boundary(domain,lambda x:np.isclose(x[0],L),tag=2)
+            model.fix(f,on=left,value=0.)
+            model.traction((traction,0.),on=right)
+    result=model.step(target=f,solver_options=SOLVER,output=folder/"fields.xdmf").solve_result()
+    result.verify("engineering").require()
+    result.write_manifest(folder/"result.json")
+    coords=f.value.function_space.tabulate_dof_coordinates()
+    values=f.value.x.array.reshape(len(coords),-1).copy()
+    assert np.isfinite(values).all()
+    np.savez_compressed(folder/"field.npz",coordinates_m=coords,values=values)
+    info={"parameters":list(map(float,p)),"refinement":refinement,"seconds":time.monotonic()-started,
+          "field_dofs":int(values.size),"max_field":float(np.max(np.linalg.norm(values,axis=1))) if kind!="heat" else float(values.max()),
+          "min_field":float(values.min())}
+    if kind=="cylinder":
+        r=coords[:,0]; A=pressure*a*a/(b*b-a*a); B=pressure*a*a*b*b/(b*b-a*a)
+        exact=(1+nu)/E*((1-2*nu)*A*r+B/r)
+        info["relative_nodal_l2_error"]=float(np.linalg.norm(values[:,0]-exact)/np.linalg.norm(exact))
+        assert info["relative_nodal_l2_error"]<.001
+    if kind=="heat" and analytical:
+        exact=10*np.sin(np.pi*coords[:,0]/L)*np.sin(np.pi*coords[:,1]/H)
+        info["relative_nodal_l2_error"]=float(np.linalg.norm(values[:,0]-300-exact)/np.linalg.norm(exact))
+        assert info["relative_nodal_l2_error"]<.01
+    (folder/"sample.json").write_text(json.dumps(info,indent=2))
+    return info
+
+def parameters(kind,n):
+    bounds={"heat":([.08,.05,15,.15,.15,.55,.55,.004,.004,1e6,1e6],[.16,.1,200,.45,.45,.85,.85,.012,.012,8e6,8e6]),
+        "notch":([.12,.04,.12,70e9,.22,10e6],[.2,.07,.30,210e9,.34,50e6]),
+        "cylinder":([.02,1.3,70e9,.22,1e6],[.08,2.8,210e9,.34,20e6])}
+    low,high=bounds[kind]
+    vals=qmc.scale(qmc.Sobol(len(low),scramble=True,seed=20260923).random_base2(int(np.log2(n))),low,high)
+    if kind=="notch": vals[:,2]*=vals[:,1]
+    if kind=="cylinder": vals[:,1]*=vals[:,0]
+    return vals
+
+if __name__=="__main__":
+    ap=argparse.ArgumentParser(); ap.add_argument("kind",choices=["heat","notch","cylinder"])
+    ap.add_argument("--count",type=int,default=256); ap.add_argument("--validate",action="store_true")
+    ap.add_argument("--refinement",type=int,default=1)
+    ap.add_argument("--start",type=int,default=0); ap.add_argument("--stop",type=int,default=None)
+    args=ap.parse_args(); assert args.count>0 and args.count&(args.count-1)==0
+    root=ROOT/"generated"/(args.kind if args.refinement==1 else args.kind+f"_r{args.refinement}")
+    if args.validate:
+        p=parameters(args.kind,4)[0]
+        a=run(args.kind,p,root/"validation"/"coarse",analytical=args.kind=="heat")
+        b=run(args.kind,p,root/"validation"/"fine",2,analytical=args.kind=="heat")
+        report={"coarse":a,"fine":b,"relative_max_field_change":abs(a["max_field"]-b["max_field"])/abs(b["max_field"])}
+        (root/"validation.json").write_text(json.dumps(report,indent=2)); print(json.dumps(report),flush=True)
+    else:
+        rows=[]
+        order=np.random.default_rng(20260924).permutation(args.count)
+        test=set(order[:args.count//8]); validation=set(order[args.count//8:args.count//4])
+        for i,p in enumerate(parameters(args.kind,args.count)):
+            if i<args.start or i>=(args.stop if args.stop is not None else args.count): continue
+            dest=root/"samples"/f"{i:05d}"
+            info=json.loads((dest/"sample.json").read_text()) if (dest/"sample.json").exists() else run(args.kind,p,dest,args.refinement)
+            info={"id":f"{i:05d}","split":"test" if i in test else "validation" if i in validation else "train",**info}
+            rows.append(info)
+            if i%16==0: print(args.kind,i+1,"/",args.count,flush=True)
+        paths=[root/"samples"/f"{i:05d}"/"sample.json" for i in range(args.count)]
+        if all(p.exists() for p in paths):
+            rows=[{"id":f"{i:05d}","split":"test" if i in test else "validation" if i in validation else "train",**json.loads(p.read_text())} for i,p in enumerate(paths)]
+            (root/"index.json").write_text(json.dumps(rows,indent=2))
+            print("COMPLETE",args.kind,len(rows),flush=True)
+        else: print("SHARD COMPLETE",args.kind,args.start,args.stop,flush=True)

load_example.py

@@ -0,0 +1,18 @@
+"""Load one full-field sample; no FEM or AI runtime needed. Apache-2.0."""
+import argparse, json
+import h5py
+from huggingface_hub import hf_hub_download
+
+if __name__=="__main__":
+    p=argparse.ArgumentParser(); p.add_argument("--repo",required=True)
+    p.add_argument("--split",choices=["train","validation","test"],default="test")
+    args=p.parse_args()
+    path=hf_hub_download(args.repo,filename=args.split+".h5",repo_type="dataset")
+    with h5py.File(path) as f:
+        sample_id=sorted(f.keys())[0]; sample=f[sample_id]
+        print("Sample",sample_id, json.loads(sample.attrs["parameters_json"]))
+        print("Coordinates:",sample["coordinates"].shape,"Connectivity:",sample["connectivity"].shape)
+        for location in ["point","cell"]:
+            if location in sample:
+                for name,field in sample[location].items():
+                    print(location,name,field.shape,"range:",float(field[:].min()),float(field[:].max()))

quality.json

@@ -0,0 +1,192 @@
+{
+  "samples": 256,
+  "unique_parameter_sets": 256,
+  "unique_geometry_or_source_layouts": 256,
+  "splits": {
+    "train": 192,
+    "validation": 32,
+    "test": 32
+  },
+  "finite_fields": true,
+  "essential_boundary_checks": true,
+  "mesh_audit": [
+    {
+      "id": "00000",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 1.4847899912619482
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 1.489582752781189
+      },
+      "relative_change": 0.003217519476707256,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00031",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 13.130352581976751
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 13.144133336903963
+      },
+      "relative_change": 0.0010484338962478208,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00064",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 6.449959427502677
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 6.457161339884976
+      },
+      "relative_change": 0.0011153372206784621,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00095",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 8.852191441456114
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 8.86428556135104
+      },
+      "relative_change": 0.0013643648787282593,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00128",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 2.642410012842788
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 2.6463305742236116
+      },
+      "relative_change": 0.0014815085533952063,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00159",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 16.207401430590494
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 16.245398915255464
+      },
+      "relative_change": 0.002338969012899287,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00192",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 8.30473183514772
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 8.32486108607202
+      },
+      "relative_change": 0.002417968386040264,
+      "quantity": "peak_temperature_rise_k"
+    },
+    {
+      "id": "00255",
+      "coarse": {
+        "nodes": 1617,
+        "cells": 3072,
+        "peak_temperature_rise_k": 2.3705533064181736
+      },
+      "fine": {
+        "nodes": 6305,
+        "cells": 12288,
+        "peak_temperature_rise_k": 2.381069604919446
+      },
+      "relative_change": 0.004416627921983127,
+      "quantity": "peak_temperature_rise_k"
+    }
+  ],
+  "metric": "peak_temperature_rise_k",
+  "metric_range": [
+    0.4318544339909067,
+    50.79281889623854
+  ],
+  "total_mesh_nodes": 413952,
+  "total_mesh_cells": 786432,
+  "versions": {
+    "agentfem": "0.3.3",
+    "fenics-dolfinx": "0.11.0",
+    "numpy": "2.4.6",
+    "scipy": "1.17.1",
+    "gmsh": "4.15.2",
+    "h5py": "3.16.0"
+  },
+  "manufactured_solution": {
+    "coarse": {
+      "parameters": [
+        0.14935717567801476,
+        0.05686161881312728,
+        179.96084503363818,
+        0.4420019393786788,
+        0.36573782516643405,
+        0.845142047572881,
+        0.7309336544945836,
+        0.007995317585766316,
+        0.004175202459096908,
+        3053588.6706784368,
+        1635806.3369989395
+      ],
+      "seconds": 2.054537249961868,
+      "field_dofs": 1617,
+      "max_field": 309.9958665179615,
+      "min_field": 300.0,
+      "relative_nodal_l2_error": 0.0004257570925223122
+    },
+    "fine": {
+      "parameters": [
+        0.14935717567801476,
+        0.05686161881312728,
+        179.96084503363818,
+        0.4420019393786788,
+        0.36573782516643405,
+        0.845142047572881,
+        0.7309336544945836,
+        0.007995317585766316,
+        0.004175202459096908,
+        3053588.6706784368,
+        1635806.3369989395
+      ],
+      "seconds": 0.07569224992766976,
+      "field_dofs": 6305,
+      "max_field": 309.99896641854036,
+      "min_field": 300.0,
+      "relative_nodal_l2_error": 0.00010646819619718235
+    },
+    "relative_max_field_change": 9.999712627116483e-06
+  }
+}

quality.py

@@ -0,0 +1,48 @@
+"""Independent field checks and mesh-refinement audits; no training-set tuning."""
+import json
+from pathlib import Path
+import numpy as np
+import h5py
+from generate import ROOT, run, parameters
+
+def metrics(kind,folder,p):
+    with h5py.File(folder/"fields.h5") as f:
+        coords=f["Mesh/ReferenceGeometry"][:]
+        cells=f["Mesh/Topology"][:]
+        out={"nodes":len(coords),"cells":len(cells)}
+        def finite(name,obj):
+            if isinstance(obj,h5py.Dataset): assert np.isfinite(obj[:]).all(),name
+        f.visititems(finite)
+        if kind=="notch":
+            center=coords[cells[:,:3]].mean(1)
+            v=f["Frames/0000/Cell/MISES"][:]
+            region=(center[:,0]>.3*p[0])&(center[:,0]<.7*p[0])
+            out["central_cell_mises_max_pa"]=float(v[region].max())
+        elif kind=="heat":
+            vals=np.load(folder/"field.npz")["values"][:,0]
+            assert vals.min()>299.99
+            out["peak_temperature_rise_k"]=float(vals.max()-300)
+        else:
+            out["radial_displacement_max_m"]=float(f["Frames/0000/Point/U"][:,0].max())
+        return out
+
+if __name__=="__main__":
+    import argparse
+    ap=argparse.ArgumentParser(); ap.add_argument("kind"); ap.add_argument("--refinement",type=int,default=1)
+    args=ap.parse_args(); kind=args.kind; ref=args.refinement
+    root=ROOT/"generated"/(kind if ref==1 else kind+f"_r{ref}")
+    pars=parameters(kind,256)
+    indices=[0,31,64,95,128,159,192,255]
+    audit=[]
+    for i in indices:
+        base=root/"samples"/f"{i:05d}"
+        if not (base/"sample.json").exists():
+            base=root/"mesh_audit_coarse"/f"{i:05d}"
+            run(kind,pars[i],base,ref)
+        fine=root/"mesh_audit"/f"{i:05d}"
+        if not (fine/"sample.json").exists(): run(kind,pars[i],fine,2*ref)
+        a=metrics(kind,base,pars[i]); b=metrics(kind,fine,pars[i]); key=list(a)[-1]
+        err=abs(a[key]-b[key])/abs(b[key])
+        audit.append({"id":f"{i:05d}","coarse":a,"fine":b,"relative_change":err,"quantity":key})
+        print(kind,i,"mesh_change",err,flush=True)
+    (root/"mesh_audit.json").write_text(json.dumps(audit,indent=2))

test.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:627341c89843e750a114aafefa528268fc7a9f6c36b0f227007ba13352942a6c
+size 1888323

test.jsonl

@@ -0,0 +1,32 @@
+{"id": "00016", "split": "test", "hdf5": "test.h5", "group": "00016", "length_m": 0.14107235983014108, "height_m": 0.06388674038462341, "conductivity_w_m_k": 117.17327440157533, "source1_x_fraction": 0.1860070450231433, "source1_y_fraction": 0.3767963167279959, "source2_x_fraction": 0.6156810212880374, "source2_y_fraction": 0.5999693837948143, "source1_sigma_m": 0.008575550109148025, "source2_sigma_m": 0.006186503492295742, "source1_peak_w_m3": 3846950.9817659855, "source2_peak_w_m3": 1429578.3713459969, "peak_temperature_rise_k": 3.156558370175844, "nodes": 1617, "cells": 3072, "parameter_sha256": "3ae03844aa52d75344be3938ec3029cf644a6962f92693b0ff3ea36386ef02b7"}
+{"id": "00024", "split": "test", "hdf5": "test.h5", "group": "00024", "length_m": 0.15246883548796178, "height_m": 0.05441937311552465, "conductivity_w_m_k": 59.91628982592374, "source1_x_fraction": 0.2318203255534172, "source1_y_fraction": 0.25096886018291115, "source2_x_fraction": 0.6464039497077465, "source2_y_fraction": 0.6713745755143463, "source1_sigma_m": 0.006142071634531021, "source2_sigma_m": 0.009781334824860096, "source1_peak_w_m3": 2031692.5635561347, "source2_peak_w_m3": 6584073.092788458, "peak_temperature_rise_k": 11.433123269546343, "nodes": 1617, "cells": 3072, "parameter_sha256": "9a8dbb1e01c180669496d3e9092bc5af1d8fb97b304f24da0ea518c37b7c80de"}
+{"id": "00030", "split": "test", "hdf5": "test.h5", "group": "00030", "length_m": 0.11448160275816918, "height_m": 0.05785839958116412, "conductivity_w_m_k": 129.38835823442787, "source1_x_fraction": 0.355228035338223, "source1_y_fraction": 0.426931733172387, "source2_x_fraction": 0.7091055712662637, "source2_y_fraction": 0.6584780267439783, "source1_sigma_m": 0.007579789027571678, "source2_sigma_m": 0.00829337563365698, "source1_peak_w_m3": 3271163.5353043675, "source2_peak_w_m3": 7150150.515139103, "peak_temperature_rise_k": 5.1143834051, "nodes": 1617, "cells": 3072, "parameter_sha256": "6f3edc7d97885e23c945068080dd1c95e38edb939a4baec96630d785c7494132"}
+{"id": "00048", "split": "test", "hdf5": "test.h5", "group": "00048", "length_m": 0.144656495526433, "height_m": 0.05166061744093895, "conductivity_w_m_k": 94.81602482032031, "source1_x_fraction": 0.4069921787828207, "source1_y_fraction": 0.4475062457844615, "source2_x_fraction": 0.8188412388786673, "source2_y_fraction": 0.8421571861952544, "source1_sigma_m": 0.00465178345143795, "source2_sigma_m": 0.009696146354079248, "source1_peak_w_m3": 3686287.336051464, "source2_peak_w_m3": 3525903.144851327, "peak_temperature_rise_k": 2.459656088010547, "nodes": 1617, "cells": 3072, "parameter_sha256": "9951ec47bc384936e90d763a2b7b77f5eafd66764c7af98bc9b1343c235a7b47"}
+{"id": "00054", "split": "test", "hdf5": "test.h5", "group": "00054", "length_m": 0.10229456678032875, "height_m": 0.06056841635145247, "conductivity_w_m_k": 163.9486444601789, "source1_x_fraction": 0.20828990098088979, "source1_y_fraction": 0.23396948128938677, "source2_x_fraction": 0.6073469896800816, "source2_y_fraction": 0.7776984246447682, "source1_sigma_m": 0.005089485436677933, "source2_sigma_m": 0.008192039519548416, "source1_peak_w_m3": 1561562.4841302633, "source2_peak_w_m3": 4082686.295732856, "peak_temperature_rise_k": 1.8667042119409416, "nodes": 1617, "cells": 3072, "parameter_sha256": "4ee83a65ae1746f2993837755af33dbf4afb882cefb966dd7d6f44db1d2ac863"}
+{"id": "00073", "split": "test", "hdf5": "test.h5", "group": "00073", "length_m": 0.1013455530256033, "height_m": 0.0782028565183282, "conductivity_w_m_k": 27.721593999303877, "source1_x_fraction": 0.4049689161591232, "source1_y_fraction": 0.17041327636688947, "source2_x_fraction": 0.6614872548729182, "source2_y_fraction": 0.813348891492933, "source1_sigma_m": 0.0057462845593690874, "source2_sigma_m": 0.010680898554623126, "source1_peak_w_m3": 4876358.669251204, "source2_peak_w_m3": 5094991.387799382, "peak_temperature_rise_k": 20.74132903369525, "nodes": 1617, "cells": 3072, "parameter_sha256": "89a5ac6945a75f3beadf2c711b3d97bfee18d38e6577abd8bbefdc06bcef623b"}
+{"id": "00076", "split": "test", "hdf5": "test.h5", "group": "00076", "length_m": 0.13735888786613942, "height_m": 0.07308906628750265, "conductivity_w_m_k": 50.5265271756798, "source1_x_fraction": 0.33606831170618534, "source1_y_fraction": 0.4092010173015297, "source2_x_fraction": 0.8406923318281769, "source2_y_fraction": 0.5525485285557807, "source1_sigma_m": 0.0064820491150021555, "source2_sigma_m": 0.008383334897458555, "source1_peak_w_m3": 5462634.376250207, "source2_peak_w_m3": 6257878.634147346, "peak_temperature_rise_k": 12.095935805356078, "nodes": 1617, "cells": 3072, "parameter_sha256": "1eb64bba578654bbf5710d3bf9334c0948314ea499c89f22aa72d598dfdfe257"}
+{"id": "00082", "split": "test", "hdf5": "test.h5", "group": "00082", "length_m": 0.08130168624222278, "height_m": 0.07444671816192568, "conductivity_w_m_k": 69.30642980150878, "source1_x_fraction": 0.3853056482970715, "source1_y_fraction": 0.3110200728289783, "source2_x_fraction": 0.7047667961567641, "source2_y_fraction": 0.7806777290999889, "source1_sigma_m": 0.006069454059004784, "source2_sigma_m": 0.004022987812757492, "source1_peak_w_m3": 6116855.165921152, "source2_peak_w_m3": 2528064.7231265903, "peak_temperature_rise_k": 5.600587712701099, "nodes": 1617, "cells": 3072, "parameter_sha256": "56111aeb6b4e45390d72559ac88b4b40ecf2131aaf1bf7536a305256c97eeeaf"}
+{"id": "00086", "split": "test", "hdf5": "test.h5", "group": "00086", "length_m": 0.10425621084868908, "height_m": 0.052147516887635, "conductivity_w_m_k": 151.31675068754703, "source1_x_fraction": 0.22661930909380318, "source1_y_fraction": 0.3820593045093119, "source2_x_fraction": 0.7461385852657259, "source2_y_fraction": 0.7301527757197619, "source1_sigma_m": 0.00806962138414383, "source2_sigma_m": 0.011403981871902943, "source1_peak_w_m3": 1086864.9408221245, "source2_peak_w_m3": 5243192.298337817, "peak_temperature_rise_k": 3.546651388305577, "nodes": 1617, "cells": 3072, "parameter_sha256": "c3698e321c2adebbc7fb0524d7f980cc32c0c2e9934fd76c6b22d1c6545895b8"}
+{"id": "00088", "split": "test", "hdf5": "test.h5", "group": "00088", "length_m": 0.1502046110481024, "height_m": 0.06989384475164116, "conductivity_w_m_k": 141.85249402187765, "source1_x_fraction": 0.43238915456458926, "source1_y_fraction": 0.42641087863594296, "source2_x_fraction": 0.5788126502186061, "source2_y_fraction": 0.8307024354115129, "source1_sigma_m": 0.0070894923359155655, "source2_sigma_m": 0.005014881141483784, "source1_peak_w_m3": 2538085.5137482285, "source2_peak_w_m3": 4184195.05469501, "peak_temperature_rise_k": 1.793424630275183, "nodes": 1617, "cells": 3072, "parameter_sha256": "79b23b4ee10b6098b1f4285ca7ce632fe8b456cbcb62b8baf1e66d492dd67de3"}
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