Francesco Piccialli

Advancements and Future Directions in Loss Function Designs for Physics-Informed Neural Networks: A Comprehensive Review

Abstract This review synthesizes recent advances in loss function designs for Physics-Informed Neural Networks (PINNs), a transformative approach to solving partial differential equations (PDEs) by embedding physical laws into deep learning frameworks. We begin by exploring the f...