3 papers
cs.LG2025
Stabilizing PINNs: A regularization scheme for PINN training to avoid unstable fixed points of dynamical systems
Milos Babic, Franz M. Rohrhofer, Bernhard C. Geiger
It was recently shown that the loss function used for training physics-informed neural networks (PINNs) exhibits local minima at solutions corresponding to fixed points of dynamica…
cs.LG2025
B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling
Kevin Innerebner, Franz M. Rohrhofer, Bernhard C. Geiger
Training physics-informed neural networks (PINNs) for forward problems often suffers from severe convergence issues, hindering the propagation of information from regions where the…
cs.LG2024
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
Franz M. Rohrhofer, Stefan Posch, Clemens GöÃnitzer +1
This paper employs physics-informed neural networks (PINNs) to solve Fisher's equation, a fundamental reaction-diffusion system with both simplicity and significance. The focus is…