Learning viscoplastic constitutive behavior via physics-informed recurrent neural network
A novel physics-informed recurrent neural network (PIRNN) model has been developed for data-driven constitutive modeling of metallic materials under high-strain-rate loading. The proposed PIRNN framework integrates a gated recurrent unit (GRU) network and a fully connected neural network to capture the rate-dependent s...