Yong Zhang

GAC-PINN: Geometry-Adaptive and Constraint-Enhanced Physics-Informed Neural Networks

For systems with steep gradients, sharp interfaces, or severe spatio-temporal coupling, Physics-informed neural networks (PINNs) suffer from spectral bias, geometric inflexibility, and boundary constraint conflicts, which undermine accuracy and convergence. To overcome these issues, we propose a geometry-adaptive and c...