2 papers
cs.LG2026
Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
MiloÅ¡ BabiÄ, 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.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…