Hyperelastic Material

Hyperelastic materials, exhibiting large, nonlinear deformations under stress, are being intensely studied to improve modeling accuracy and efficiency. Current research focuses on developing and applying physics-informed neural networks (PINNs), meshfree methods, and generative models to calibrate constitutive parameters from full-field experimental data, often bypassing traditional computationally expensive methods like finite element analysis. These advancements enable faster and more robust forward and inverse modeling, with applications ranging from structural health monitoring to the design of soft robots and bio-inspired materials. The resulting improvements in material characterization and simulation are crucial for diverse engineering and scientific fields.