executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
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periodic_pde_benchmark
periodic_pde_benchmarkBuild deterministic periodic PDE benchmarks for PINN failure-mode recovery experiments.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentation de la Skill
GitHubname: periodic_pde_benchmark description: Build deterministic periodic PDE benchmarks for PINN failure-mode recovery experiments.
Periodic PDE Benchmark
Use this skill when a recovery or evaluation task needs a small synthetic PDE benchmark matching the PINN failure-mode paper. Do not use it for unrelated PDE families or as evidence of full paper-scale reproduction.
Inputs
- PDE system and coefficients, currently convection for deterministic reduced recovery.
- Grid sizes, collocation count, and random seed.
- Periodic spatial domain and time horizon assumptions.
Outputs
- A JSON-compatible benchmark with evaluation grid, exact target values, collocation points, boundary pairs, and initial-condition samples.
- Metadata identifying coefficient values and exact/proxy target type.
Workflow
- Choose the smallest system that tests the paper mechanism, usually periodic convection.
- Build
xandtgrids on[0, 2π] × [0, 1]. - Compute
u(x,t)=sin(x-beta*t)for exact convection targets. - Generate seeded collocation points and periodic boundary pairs.
- Save the benchmark or pass it directly to residual-objective and diagnostics skills.
Validation
Run python scripts/benchmark.py --beta 30 --x-points 8 --t-points 4 --collocation-count 5 and run the tests in tests/.
Limitations
This skill provides a reduced exact convection benchmark. Full reaction-diffusion data generation should extend the same contract and declare numerical target provenance.