HYPERPARAMTER OPTIMIZATION FOR CAUSAL MARCHING PHYSICS INFORMED NEURAL NETWORK FOR HYPERELASTICITY

This study presents an approach for hyperparameter optimization in the Causal-Marching Physics-Informed Neural Networks (CMPINNs) framework, specifically designed to model hyperelasticity. Physics-Informed Neural Networks (PINNs) are powerful tools for solving governing partial differential equations (PDEs) in physical systems. The CMPINNs model proposed in this work enhances the PINN framework by minimizing the residuals of the governing PDEs while enforcing the boundary conditions for the nonlinear mechanical responses of hyperelasticity. We study the accuracy of using CMPINNs to solve the Neo-Hookean hyperelastic model using soft and hard constrained boundary conditions. Additionally, the study presented a hyperparameter optimization for CMPINNs to identify the best suitable set of hyperparameters for deformation like biaxial compression. This optimization process ensures that the CMPINN effectively captures the complex stress-strain relationships in hyperelastic materials under deformation. This research advances the development of robust, physics-informed computational models for hyperelastic materials, reducing reliance on labelled or synthetic data.