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Franz M. Rohrhofer
Franz M. Rohrhofer
PhD Researcher at Know-Center GmbH
Bestätigte E-Mail-Adresse bei know-center.at
Titel
Zitiert von
Zitiert von
Jahr
On the Apparent Pareto Front of Physics-informed Neural Networks
FM Rohrhofer, S Posch, C Gößnitzer, BC Geiger
IEEE Access, 2023
52*2023
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks
FM Rohrhofer, S Posch, C Gößnitzer, BC Geiger
Transactions on Machine Learning Research, 2023
33*2023
Theory-inspired machine learning—towards a synergy between knowledge and data
JG Hoffer, AB Ofner, FM Rohrhofer, M Lovrić, R Kern, S Lindstaedt, ...
Welding in the World 66 (7), 1291-1304, 2022
162022
How PINNs cheat: Predicting chaotic motion of a double pendulum
S Steger, FM Rohrhofer, BC Geiger
The Symbiosis of Deep Learning and Differential Equations II, 2022
42022
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
FM Rohrhofer, S Saha, S Di Cataldo, BC Geiger, W von der Linden, ...
arXiv preprint arXiv:2102.00191, 2021
32021
Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes
FM Rohrhofer, S Posch, C Gößnitzer, JM García-Oliver, B Geiger
Scientific Computing 2023, 4-11, 2023
12023
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
FM Rohrhofer, S Posch, C Gößnitzer, BC Geiger
arXiv preprint arXiv:2402.08313, 2024
2024
Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion
FM Rohrhofer, S Posch, C Gößnitzer, JM García-Oliver, BC Geiger
Energy and AI, 100341, 2024
2024
Finding the Optimum Design of Large Gas Engines Prechambers using CFD and Bayesian Optimization
S Posch, C Gößnitzer, FM Rohrhofer, B Geiger, A Wimmer
Scientific Computing 2023, 160-168, 2023
2023
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