Franz M. Rohrhofer

According to our database, Franz M. Rohrhofer authored at least 8 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks.

Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C. Geiger

CoRR, 2024

2023

On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks.

Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C. Geiger

Trans. Mach. Learn. Res., 2023

Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes.

Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, José M. García-Oliver, Bernhard C. Geiger

CoRR, 2023

Finding the Optimum Design of Large Gas Engines Prechambers Using CFD and Bayesian Optimization.

Stefan Posch, Clemens Gößnitzer, Franz M. Rohrhofer, Bernhard C. Geiger, Andreas Wimmer

CoRR, 2023

Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks.

Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C. Geiger

IEEE Access, 2023

2022

Understanding the Difficulty of Training Physics-Informed Neural Networks on Dynamical Systems.

Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer, Bernhard C. Geiger

CoRR, 2022

2021

On the Pareto Front of Physics-Informed Neural Networks.

Franz M. Rohrhofer, Stefan Posch, Bernhard C. Geiger

CoRR, 2021

Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures.

Franz M. Rohrhofer, Santanu Saha, Simone Di Cataldo, Bernhard C. Geiger, Wolfgang von der Linden, Lilia Boeri

CoRR, 2021