HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators

arXiv:2404.07943v1 Announce Type: cross
Abstract: Homogenization is an essential tool for studying multiscale physical phenomena. However, traditional numerical homogenization, heavily reliant on finite element analysis, requires extensive computation costs, particularly in handling complex geometries, materials, and high-resolution problems. To address these limitations, we propose a numerical homogenization model based on operator learning: HomoGenius. The proposed model can quickly provide homogenization results for arbitrary geometries, materials, and resolutions, increasing the efficiency by a factor of 80 compared to traditional numerical homogenization …

abstract analysis arxiv computation costs cs.ce cs.lg element foundation foundation model however limitations materials numerical operators prediction resolution studying tool type