Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization
The research developed a Polyconvex Physics-Augmented Neural Network (PANN) to create accurate, fast models for anisotropic materials. It learns complex material behavior by adapting directional features while mathematically guaranteeing the resulting energy function is polyconvex and objective, bridging the gap between high accuracy and physical admissibility.