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name: pdebench description: "Guide PDEBench scientific-machine-learning benchmark workflows for PDE dataset preparation, simulation generation, baseline neural operators, PINNs, inverse models, metrics, and safe result analysis." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PDEBench

PDEBench is a scientific machine-learning benchmark package covering PDE simulation data, HDF5 datasets, FNO/U-Net/PINN baselines, inverse methods, and metrics. Use this skill when a task mentions PDEBench, its PDE names or dataset files, neural operators for PDEs, velocity2vorticity, Hydra PDE generation, or the benchmark's forward/inverse model configuration.

Choose a route

• Dataset files, metadata, visualization, or velocity-to-vorticity: read data-and-download. It owns local HDF5 schemas, safe metadata checks, the converter, and spectral-vorticity APIs.
• PDE simulation or NLE/JAX generation: read data-generation. It owns Hydra config selection, bounded generation planning, NLE families, and NPY-to-HDF5 merge boundaries.
• Forward/inverse baselines, metrics, or result analysis: read models-and-evaluation. It owns FNO, U-Net, PINN, inverse, Hydra training/evaluation, and metric routing.

For a task that spans routes, start here, then load each relevant sub-skill in upstream-to-downstream order: data layout → generation or retrieval → model or evaluation.

Install and inspect

The public distribution is pdebench and the repository declares Python >=3.9,<3.11. A minimal install is:

python -m pip install pdebench
python -c "import pdebench; print('pdebench import ok')"

For the legacy-compatible package stack, use a Python 3.9 or 3.10 isolated environment and let the package metadata select its base dependencies. The datagen39 and datagen310 extras add optional Clawpack, PhiFlow, JAX, and CUDA-oriented dependencies; install one only when a specific generation route needs it. They are not required for metadata checks or the baseline CPU API smokes.

PINN workflows use DeepXDE. Select the PyTorch backend before importing the PINN modules when more than one DeepXDE backend is installed:

DDE_BACKEND=pytorch python -c "import deepxde; print(deepxde.backend.backend_name)"

PyTorch and JAX code can use CUDA when a compatible backend build is installed. The verified baseline for this skill is CPU PyTorch/JAX; do not claim CUDA runtime coverage from a CPU import.

Shared operating rules

1. Identify the PDE family, data filename/layout, model route, and expected output before constructing a command.
2. Read the nearest linked reference before using a long parameter list or HDF5 schema. Keep data paths explicit; Hydra may change the working directory during a run.
3. Start with --help, config rendering, a local metadata check, or a tiny deterministic smoke. Do not download multi-GB/TB datasets, upload to DaRUS, generate benchmark-scale data, or train for hundreds of epochs by default.
4. Treat data_path, filename, single_file, resolutions, initial_step, and model-specific channel counts as a coupled contract. Validate shapes before training or evaluation.
5. Keep output directories new and explicit. Never overwrite checkpoints or converted HDF5 files without an intentional --overwrite-style decision.
6. Use cross-cutting troubleshooting for install, optional dependency, backend, path, and artifact problems.

Provenance and limits

Read repository provenance before deciding whether this skill matches a current checkout or whether a refresh is needed. Read installation and compatibility when versions, optional extras, or backends are unclear. This operating graph covers public package workflows; it does not certify benchmark scores, replace large datasets, or make network and credential decisions for the user.