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name: models-and-evaluation description: "Route PDEBench forward and inverse model configuration, safe model checks, metrics, checkpoints, and result analysis without reopening the source checkout." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Models and evaluation

Use this sub-skill when a Researcher must choose, configure, inspect, or adapt PDEBench baseline models and their evaluation outputs. It covers the FNO, U-Net, PINN, and gradient/ProbRasterLatent/InitialConditionInterp inverse paths. It does not download data, generate PDE data, visualize datasets, or run a benchmark by default.

Route by question

• Choose a forward model or check tensor/layout contracts: model-overview.md.
• Construct a Hydra override, select train/eval/checkpoint mode, or make a CPU/GPU decision: configuration.md.
• Compute metrics, understand pickle/CSV artifacts, or compare plots: metrics-and-results.md.
• Diagnose imports, backend selection, paths, shapes, checkpoints, memory, or result failures: troubleshooting.md.
• Run only the deterministic, no-epoch smoke check: scripts/model_smoke.py.

Operating rules

1. Establish the dataset filename, dimensionality, variable/channel count, initial context length, desired forward or inverse objective, and the intended output/checkpoint location before changing a config.
2. Keep PDEBench's data convention visible: arrays are generally [batch, x1, ..., xd, time, variables] at dataset boundaries, while FNO consumes a flattened time-variable context plus a coordinate grid and U-Net consumes channel-first tensors. Do not silently transpose data.
3. Use if_training: false for evaluation only when the matching checkpoint is already present. Use continue_training: true only with the matching optimizer-bearing checkpoint. A smoke check is not model training.
4. Treat full training, MCMC, PINN optimization, checkpoint recovery, and benchmark comparisons as user-authorized, data-dependent, potentially long-running operations. Do not fetch pretrained weights or datasets by default.
5. For PINN, select and verify the DeepXDE PyTorch backend before importing pdebench.models.pinn.train; for inverse MCMC, separately verify Pyro.
6. When a source implementation caveat is encountered, preserve the caveat in the run notes rather than silently “fixing” the model contract. Use the troubleshooting reference to decide whether a local, user-approved patch is needed.

Evidence boundary

This graph distills the published README's “Baseline Models”, “Short explanations on the config args”, and model configuration documentation, plus observed contracts in the installed module family pdebench.models.train_models_forward, pdebench.models.train_models_inverse, the fno, unet, pinn, and inverse modules, metrics, the result-analysis programs, and the published shell workflow examples. Those source artifacts are provenance only; they are not bundled runtime files. Dataset downloading, data generation, visualization, and vorticity are intentionally routed to other sub-skills.

Safe verification

Set SKILL_ROOT to the directory containing this sub-skill's SKILL.md, then run the bundled smoke with the installed pdebench package:

python "$SKILL_ROOT/scripts/model_smoke.py" --help
python "$SKILL_ROOT/scripts/model_smoke.py"

The script constructs tiny FNO/U-Net modules on CPU and checks metric output; it never opens a dataset, loads a checkpoint, starts an epoch, calls DeepXDE, or invokes Pyro. Do not use a source checkout path as an import workaround. For any real run, first apply the configuration and failure-recovery guidance in the linked references.