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?"

Convert per-case Abaqus FEA outputs into ML-ready (X, Y) wide-table CSVs. Pivots irregular FEA mesh node displacements onto a regular N×N grid via direct binning (structured mesh) or bilinear resampling, picks the final frame as the deformation target, and aggregates across many cases into X_amplitude.csv (design vectors) + Y_grid_uz.csv (flattened grid displacement). Use when the user has a folder of completed FEA cases and wants to train a Ridge / MLP / Gaussian Process surrogate on the (input → displacement field) mapping.