2 papers
cs.LG2026
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
Sebastien Andre-Sloan, Dibyakanti Kumar, Alejandro F Frangi +1
This work establishes rigorous first-of-its-kind upper bounds on the generalization error for the method of approximating solutions to the (d+1)-dimensional incompressible Navier-S…
cs.LG2026
Noisy PDE Training Requires Bigger PINNs
Sebastien Andre-Sloan, Anirbit Mukherjee, Matthew Colbrook
Physics-Informed Neural Networks (PINNs) are increasingly used to approximate solutions of partial differential equations (PDEs), particularly in high dimensions. In real-world set…