Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework

arXiv:2404.17583v1 Announce Type: cross
Abstract: In this work, a hybrid physics-based data-driven surrogate model for the microscale analysis of heterogeneous material is investigated. The proposed model benefits from the physics-based knowledge contained in the constitutive models used in the full-order micromodel by embedding them in a neural network. Following previous developments, this paper extends the applicability of the physically recurrent neural network (PRNN) by introducing an architecture suitable for rate-dependent materials in a finite strain framework. In this model, the …

abstract analysis arxiv benefits cond-mat.mtrl-sci cs.lg cs.na data data-driven embedding framework hybrid knowledge material materials math.na network neural network path physics rate recurrent neural network them type work