1 citations · 2 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer +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…
Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer +2
Flamelet models are widely used in computational fluid dynamics to simulate thermochemical processes in turbulent combustion. These models typically employ memory-expensive lookup…