2 papers
cs.LG2026
Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
Miloš Babić, Franz M. Rohrhofer, Stefan Posch
From neural ODEs to continuous-time machine learning, differentiable solvers allow physics, optimization, and simulation to become trainable components within deep learning systems…
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…