benchmark-and-mms-plannerlisted

# Benchmark And MMS Planner ## Goal Design a verification and validation plan before trusting simulation results. The skill helps agents choose manufactured solutions, benchmark cases, refinement protocols, uncertainty checks, and pass/fail criteria. ## Requirements - Python 3.10+ - No external dependencies - Works on Linux, macOS, and Windows ## Inputs to Gather | Input | Description | Example | |-------|-------------|---------| | PDE or model class | Governing family | `diffusion`, `elasticity`, `phase-field` | | Quantity of interest | Metric to validate | `interface velocity`, `L2 temperature error` | | Dimension | 1, 2, or 3 | `2` | | Expected order | Formal discretization order | `2` | | Reference availability | Analytic, benchmark, or none | `analytic` | | Risk level | Cost or consequence of wrong result | `high` | ## Decision Guidance - Use **MMS** when code correctness is uncertain and an analytic solution can be injected. - Use **canonical benchmarks** when physical model validation matters more than code verification. - Use **grid/time refinement** whenever the result is used for a claim, design decision, or comparison. - Use **uncertainty propagation** when inputs are calibrated, noisy, or experimentally measured. ## Script Outputs `scripts/benchmark_mms_planner.py` emits `inputs` and `results` with: - `verification_strategy` - `effective_model` — the resolved model family actually used; unknown families fall back to `general`. - `mms_plan` - `benchmark_