In this work we present a hybrid physics-based and data-driven learning approach to
construct surrogate models for concurrent multiscale simulations of complex material
behavior. We start from robust but inflexible physics-based constitutive models and increase
their expressivity by allowing a subset of their material parameters to change in time
according to an evolution operator learned from data. This leads to a flexible hybrid model
combining a data-driven encoder and a physics-based decoder. Apart from introducing …