20 citations · 51 across the 7 of their papers we have counts for
5 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…
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer +1
This paper empirically studies commonly observed training difficulties of Physics-Informed Neural Networks (PINNs) on dynamical systems. Our results indicate that fixed points whic…
Knock Detection in Combustion Engine Time Series Using a Theory-Guided 1D Convolutional Neural Network Approach
Andreas B. Ofner, Achilles Kefalas, Stefan Posch +1
This paper introduces a method for the detection of knock occurrences in an internal combustion engine (ICE) using a 1D convolutional neural network trained on in-cylinder pressure…