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.

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

@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.