A Bayesian active learning method with statistical feature engineering and multi-output Gaussian processes selects target hyperelastic metamaterial designs from 50,000 candidates using under 0.5% high-fidelity oracle calls.
Willot, Fourier-based schemes for computing the mechanical response of composites with accurate local fields, Comptes Rendus Mécanique 343 (2015) 232–245
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Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterials
A Bayesian active learning method with statistical feature engineering and multi-output Gaussian processes selects target hyperelastic metamaterial designs from 50,000 candidates using under 0.5% high-fidelity oracle calls.