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cs.LG2026
Physics-Informed Foresight Pruning for Sparse PINN Solvers of Nonlinear PDEs
Ahmad Ishaque Karimi, Uvini Balasuriya Mudiyanselage, Kookjin Lee
Physics-informed neural networks (PINNs) often rely on over-parameterized models to optimize coupled solution and differential-residual objectives, leaving unclear how much capacit…
cs.LG2026
Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs
Cheng Jing, Uvini Balasuriya Mudiyanselage, Abhishek Verma +3
Parameterized and coupled partial differential equations (PDEs) are central to modeling phenomena in science and engineering, yet neural operator methods that address both aspects…