Open access
2026
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...
Qiang Chen, Xu-Yang Zhang, Xue-Feng Chen et al.
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