Nonlinear Material

Nonlinear material modeling aims to accurately predict the complex behavior of materials under large deformations and stresses, a challenge for traditional methods. Current research focuses on developing data-driven approaches, employing neural networks (including autoencoders, generative adversarial networks, and physics-guided networks) to learn material constitutive laws from experimental data or simulations, often bypassing the need for explicit physical equations. These advanced computational techniques are improving the accuracy and efficiency of both forward (predicting material response) and inverse (designing materials with specific properties) modeling, with applications spanning diverse fields like robotics, medicine, and advanced manufacturing.