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)

On the Pareto Front of Physics-Informed Neural Networks. - Franz M. Rohrhofer; Stefan Posch; Bernhard C. Geiger (CoRR, 2021)

Understanding the Difficulty of Training Physics-Informed Neural Networks on Dynamical Systems. - Franz M. Rohrhofer; Stefan Posch; Clemens Gößnitzer; Bernhard C. Geiger (CoRR, 2022)

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)

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)

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)

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)

Approximating families of sharp solutions to Fisher's equation with physics-informed neural networks. - Franz M. Rohrhofer; Stefan Posch; Clemens Gößnitzer; Bernhard C. Geiger (Comput. Phys. Commun., 2025)

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling. - Kevin Innerebner; Franz M. Rohrhofer; Bernhard C. Geiger (CoRR, 2025)