Learning material law from displacement fields by artificial neural network
Hang Yang, Xiang Qian, Shan Tang, Xu Guo
Abstract
The recently developed data-driven approach can establish the material law for nonlinear elastic composite materials (especially newly developed materials) by the generated stress-strain data under different loading paths (Computational Mechanics, 2019). Generally, the displacement (or strain) fields can be obtained relatively easier using digital image correlation (DIC) technique experimentally, but the stress field is hard to be measured. This situation limits the applicability of the proposed data-driven approach. In this paper, a method based on artificial neural network (ANN) to identify stress fields and further obtain the material law of nonlinear elastic materials is presented, which can make the proposed data-driven approach more practical. A numerical example is given to prove the validity of the method. The limitations of the proposed approach are also discussed.