Papers „Physics-Informed Neural Networks for State Estimation Problems“ and „Tuning for Explainability“ accepted at the 35th CI-Workshop in Berlin 2025

Two papers from THK-AI members were accepted for presentation at the 35th Workshop on Computational Intelligence in Berlin, November 2025, and will be published as open access in the KIT Verlag proceedings. The first paper, by Aleksandr Subbotin and Thomas Bartz-Beielstein, explores combining machine learning with classical filtering methods. The second paper, a collaboration with Everllence SE, introduces new measures to enhance explainability in artificial intelligence.