Die Oberfläche ist lokalisiert; Skill-Beschreibungen und -Dokumente werden per KI automatisch in Ihre Sprache übersetzt. Code, Befehle, Pfade und Fachbegriffe bleiben im Original, maßgeblich ist das Dokument des Autors.
Skills entdecken
Daten und Forschung · SKILL.md

data-generation

data-generation

Route safe, reproducible PDEBench simulation generation, Hydra/JAX configuration, and postprocessing without launching unbounded jobs.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: data-generation description: "Route safe, reproducible PDEBench simulation generation, Hydra/JAX configuration, and postprocessing without launching unbounded jobs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PDEBench data generation

Use this sub-skill when a researcher needs to inspect, configure, or deliberately run PDEBench simulation generators. It covers the classical generators and the NLE Hydra/JAX families. It is a routing skill, not a request to regenerate the published benchmark.

Start with a safe route

Before running anything, record:

  1. the family and entry point (classical diffusion/sorption, diffusion/reaction, incompressible Navier–Stokes, radial dam break, or NLE advection, Burgers, reaction-diffusion/Darcy, compressible CFD);
  2. the exact config group/file and overrides;
  3. the intended sample count, grid, final time, save interval, output directory, and device/backend;
  4. whether the desired artifact is per-sample HDF5, NLE .npy, or a merged HDF5 file; and
  5. whether this is help/config inspection, a tiny fixture, or an approved production run.

Default to help-only or config rendering. Copy outputs into a new, explicitly named directory. Never start a family-wide shell loop, upload, or merge as a verification shortcut. The top-level classical wrappers contain hard-coded large sample ranges (1,000, 10,000, or 10,000 samples); NLE run_*.sh files also contain multi-GPU loops and can create very large arrays.

Read the relevant internal runbook before proceeding:

The runbook distinguishes commands that work through installed package modules from source-evidence-only facts. No published checkout, native config tree, or native generator script is bundled by this skill.

Installed-package entry-point policy

The classical diffusion-reaction and diffusion-sorption wrappers are importable package modules. After installing PDEBench and its declared runtime requirements, use commands such as:

python -m pdebench.data_gen.gen_diff_react --help
python -m pdebench.data_gen.gen_diff_sorp --cfg job --resolve

The radial-dam-break wrapper is also a package module, but importing it requires the optional Clawpack stack. The incompressible Navier–Stokes wrapper and the NLE programs currently use bare native-layout imports (src or utils), so there is no verified installed-package-safe execution command for those programs in this skill. Their names, configs, and numerical controls below are source evidence only. Do not make a future agent change directory to a native family directory or copy a native script path into a runtime command. Use the bounded format checks in data and output formats and the dependency/merge diagnosis in troubleshooting as the bundled alternatives; a user-owned adapter is required for an NLE run.

Family router

  • Classical 1D diffusion-sorption: package module pdebench.data_gen.gen_diff_sorp, using the diff-sorp Hydra config group. The solver evidence is the importable helper pdebench.data_gen.src.sim_diff_sorp.
  • Classical 2D diffusion-reaction: package module pdebench.data_gen.gen_diff_react, using the diff-react config group. The solver evidence is pdebench.data_gen.src.sim_diff_react.
  • Classical 2D incompressible Navier–Stokes: the native wrapper is source evidence only because its runner imports a bare src module. The route needs optional phiflow/phi and a compatible backend.
  • Classical 2D radial dam break/shallow water: package module pdebench.data_gen.gen_radial_dam_break, using the radial_dam_break config group. This route needs optional Clawpack.
  • NLE JAX: the source-evidence program families are AdvectionEq, BurgersEq, ReactionDiffusionEq, and CompressibleFluid. Their native Hydra programs use a bare utils import and are not promised as installed-package entry points. Single-solution programs use an args config group; batched programs use a multi group.
  • Darcy merge path: the source-evidence 2D reaction-diffusion program is used by the native Darcy production loop. Treat it as an NLE production loop, not as a small Darcy test.
  • Postprocessing: the package module pdebench.data_gen.data_gen_NLE.Data_Merge is parser-inspectable when Hydra and the package data are installed, but its merge implementation has known defects documented in the runbook. Merge is not default verification.

Dataset retrieval, plotting/visualization, and velocity-to-vorticity conversion belong to the data-and-download route. FNO/U-Net/PINN/inverse training and metrics belong to models-and-evaluation. Notebooks, attic helpers, CI/release files, credentials, and network upload are not runtime dependencies here.

Minimum safe checks

With the public package installed, first prefer:

python -m pdebench.data_gen.gen_diff_react --help
python -m pdebench.data_gen.gen_diff_sorp --cfg job --resolve

Use the equivalent module-qualified help/config check for a selected classical package wrapper. For radial dam break, check that Clawpack is installed before importing the module. NLE and incompressible-NS commands are not listed as copyable runtime commands because their current native imports are not self-contained after package installation.

A help/config check must not be described as a generated dataset. The recorded verification baseline for the generated skill was Python 3.10 with Hydra 1.3.5, CPU JAX 0.4.38, and NumPy 1.26.4. This revision's live shell probe used Python 3.13 and found Hydra and JAX unavailable, so those checks were not rerun here; that live fact is a limitation, not a successful package verification.

Omission and verification boundary

This skill does not retrieve datasets, inspect network repositories, upload to Dataverse, repair source bugs, run the full benchmark generation suite, or certify numerical fidelity to the paper. Full generation, NLE runs, merge, large output production, upload, and credential use all require an explicit user-approved scope and an environment check. If a requested action crosses those limits, explain the cost and route it for approval rather than silently launching it.

Gespeicherte Skills