gpd-verify-work

You can automate physics consistency checks by running systematic verification scripts that validate dimensional consistency, limiting cases, and numerical convergence against benchmark results. This ensures the correctness of numerical outcomes and physical conclusions.

Systematic verification validates computational science results by checking dimensional consistency and numerical convergence automatically. It executes a suite of physics checks and outputs a tracking file to record all validation results for your research phase.

Yes, automated result validation requires a Python environment with NumPy and SciPy installed, along with the gpd runtime. These dependencies provide the necessary computational frameworks to execute physics checks and benchmark comparisons.

To verify research results for a specific phase, you run the verification command with the phase number as an argument. The Skill checks numerical outcomes against phase goals and contract requirements, generating a dedicated verification markdown file.

Research result validation includes physics checks for dimensional consistency, limiting cases, numerical convergence, and benchmark result comparisons. These systematic checks ensure your numerical analysis and physical conclusions align with established physical principles.

The verification approach relies heavily on comparing against benchmark results to validate physical conclusions. Without established benchmarks, the numerical convergence and limiting case checks still function, but overall validation completeness may be limited.