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data-generation【数据与研究】Route safe, reproducible PDEBench simulation generation, Hydra/JAX configuration, and postprocessing without launching unbounded jobs.
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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.
Before running anything, record:
.npy, or a merged
HDF5 file; andDefault 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.
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.
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.pdebench.data_gen.gen_diff_react, using the diff-react config group.
The solver evidence is pdebench.data_gen.src.sim_diff_react.src module. The route needs
optional phiflow/phi and a compatible backend.pdebench.data_gen.gen_radial_dam_break, using the radial_dam_break config
group. This route needs optional Clawpack.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.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.
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.
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.