Abaqus ML Skills

Generate an Abaqus FEA training dataset for surrogate / ML models. Latin Hypercube Sampling (or sparse-pattern sampling) over a parameterized design vector, one case folder per sample, batch-submit Abaqus jobs via subprocess, recover from crashes, and write a unified dataset index. Use when the user wants to "build a training set for a surrogate model", "sweep design parameters in Abaqus", "run N FEA simulations", or "sample a design space".

Closed-loop inverse-design validation. Given a target deformation field, solve the inverse problem on a trained surrogate (Ridge / linear), then run an Abaqus FEA verification and compare surrogate-predicted vs. true displacement field. Reports MSE / MAE / max-abs-error / NRMSE side-by-side, plus saturated-channel count, so you can quantify the surrogate-FEA gap. Use when the user wants to evaluate "is my surrogate good enough for inverse design?", "how big is the surrogate-FEA gap on this target?", "did the optimizer find a real solution or just a surrogate hallucination?"