A. Karimi

Physics-Informed Foresight Pruning for Sparse PINN Solvers of Nonlinear PDEs

Physics-informed neural networks (PINNs) often rely on over-parameterized models to optimize coupled solution and differential-residual objectives, leaving unclear how much capacity is necessary and what pruning should preserve. We study foresight pruning at initialization for sparse PirateNet PDE solvers. Standard neu...