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.
FE-PIRBN: Feature-enhanced physics-informed radial basis neural networks for solving high-frequency electromagnetic scattering problems,
2 Pith papers cite this work. Polarity classification is still indexing.
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A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
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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.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.