22 citations · 65 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
Finding the Optimum Design of Large Gas Engines Prechambers Using CFD and Bayesian Optimization
Stefan Posch, Clemens Gößnitzer, Franz Rohrhofer +2
The turbulent jet ignition concept using prechambers is a promising solution to achieve stable combustion at lean conditions in large gas engines, leading to high efficiency at low…
Generating Simple Directed Social Network Graphs for Information Spreading
Christoph Schweimer, Christine Gfrerer, Florian Lugstein +4
Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following…
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…