Gianluca Fabiani

Going with the flow to solve for symmetry-driven PDE dynamics with physics-informed neural networks

This work addresses a computational framework that integrates symmetry reduction into Physics-Informed Neural Networks (PINNs) for analysing symmetry-driven dynamics in nonlinear partial differential equations (PDEs), enabling accurate forward simulation and parameter inference without front tracking or mesh adaptation...