Abaqus ODB → ML-Ready Grid CSV

The post-processing half of a surrogate-model pipeline. Takes a folder of completed Abaqus FEA cases (typically produced by the abaqus-lhs-batch-dataset skill) and produces two flat CSVs ready for numpy.loadtxt / pandas.read_csv / scikit-learn:

• X_amplitude.csv — sample_id, amplitude_0000, amplitude_0001, ..., amplitude_(D-1) — the input design vectors
• Y_grid_uz.csv — sample_id, uz_0000, uz_0001, ..., uz_(N²-1) — the resulting deformation field flattened from N×N grid (row-major)

These are the two matrices a surrogate model trains on: shape (N_samples, D) and (N_samples, N²).

When to Use This Skill

Activate when the user wants to:

• Convert a directory of sample_*/Membrane2D1.odb (or node_displacement.csv) cases into ML-ready training matrices
• Resample a fine FEA mesh (e.g. 81×81) onto a coarser learning grid (e.g. 41×41 or 21×21)
• Aggregate multiple dataset runs and deduplicate by input vector
• Inspect / validate a dataset before training