# Haibarakiku

> Open-source publisher. Listings imported from github.com/Haibarakiku — credited to the original author with their license.

- **Listings:** 3
- **Total installs:** 0
- **Profile:** https://agentstack.voostack.com/s/haibarakiku
- **Website:** https://github.com/Haibarakiku

## Published listings

- [Abaqus Surrogate Fea Validation](https://agentstack.voostack.com/l/skill-haibarakiku-abaqus-ml-skills-abaqus-surrogate-fea-validation) — Skill · Free · security-reviewed — `agentstack add skill-haibarakiku-abaqus-ml-skills-abaqus-surrogate-fea-validation`
  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 evalua…
- [Abaqus Odb To Grid Csv](https://agentstack.voostack.com/l/skill-haibarakiku-abaqus-ml-skills-abaqus-odb-to-grid-csv) — Skill · Free — `agentstack add skill-haibarakiku-abaqus-ml-skills-abaqus-odb-to-grid-csv`
  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 fold…
- [Abaqus Lhs Batch Dataset](https://agentstack.voostack.com/l/skill-haibarakiku-abaqus-ml-skills-abaqus-lhs-batch-dataset) — Skill · Free — `agentstack add skill-haibarakiku-abaqus-ml-skills-abaqus-lhs-batch-dataset`
  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…

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