Operator Learning for Predicting Mechanical Response of Hierarchical Composites with Applications of Inverse Design

Abstract

Materials-by-design to develop high performance composite materials is often computational intractable due to the tremendous design space. Here, a deep operator network (DeepONet) is presented to bridge the gap between the material design space and mechanical behaviors. The mechanical response such as stress or strain can be predicted directly from material makeup efficiently, and a good accuracy is observed on unseen data even with a small amount of training data. Furthermore, the proposed approach can predict mechanical response of complex materials regardless of geometry, constitutive relations, and boundary conditions. Combined with optimization algorithms, the network offers an efficient tool to solve inverse design problems of composite materials.