2 papers
cs.AI2026
Evaluating LLMs as Interpretable Controllers for Dynamical Systems
Aleksander Østensen, Alberto Mino Calero, Anastasios M. Lekkas +1
Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems remains largely unexplored. This…
cs.LG2026
Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results
Alberto Miño Calero, Luis Salamanca, Konstantinos E. Tatsis
Physics-Informed Neural Networks (PINNs) incorporate physics into neural networks by embedding partial differential equations (PDEs) into their loss function. Despite their success…