Neural Network-Based Constitutive Model for Solid Materials

This proposes novel neural network-based approaches to reproduce the complex nonlin-ear constitutive relations of solid materials including elastic behavior, plastic deformation and damage mechanism. A history-based strategy has been suggested using an artificial neural network for training path-dependent inelastic behavior. The network development is based on a general internal formalism. of selected It is shown that the proposed methodology can represent exactly the von Mises elastoplastic material model in uni-axial stress state. The strategy was applied to sequences of training and validation data which were generated numerically for elastoplasticity with and without hardening as well as for elastoplastic damage. The results have been compared against established mathematical models and shown a potential of describing complex non-linear solid material behavior accurately in one-dimensional