4 citations · 4 across the 1 of their papers we have counts for
4 papers
Inertia-Aware Optimal Power Flow Using PINN in IBR-Dominated Power Systems
Mahyar Tofighi-Milani, Sajjad Fattaheian-Dehkordi, Franz Martin Rohrhofer +1
The problem of Optimal Power Flow (OPF) is central to the secure and economic operation of modern power systems. However, increasing renewable energy penetration, and decreasing sy…
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…
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 +3
Drive towards improved performance of machine learning models has led to the creation of complex features representing a database of condensed matter systems. The complex features,…