Tadeusz Uhl

Derivative Computation in PINNs: Automatic Differentiation, Finite Differences and Beyond

We systematically investigate finite-difference (FD) derivative computation in Physics-Informed Neural Networks (PINNs) as an alternative to automatic differentiation (AD). On three benchmark PDEs we show that, with a properly calibrated step size, FD matches AD in accuracy on every problem while running faster across...