A mechanics-informed deep learning constitutive model for sequential prediction of strain rate-dependent behavior and microstructural evolution

Classical constitutive models explicitly couple macroscopic mechanical responses with underlying microstructural evolution, which is crucial for capturing complex deformation mechanisms across varying strain rates. However, current deep learning (DL) constitutive models predominantly focus on macroscopic stress-strain mapping, often neglecting these critical microstructural transitions. To bridge this gap, this work proposes a mechanics-informed deep learning constitutive model (MIDLCM) that int