4 papers
Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
Pantelis Dogoulis, Maxime Cordy
Topology control for power grid operation is a challenging sequential decision making problem because the action space grows combinatorially with the size of the grid and action ev…
Can Large Language Models Reason and Optimize Under Constraints?
Fabien Bernier, Salah Ghamizi, Pantelis Dogoulis +1
Large Language Models (LLMs) have demonstrated great capabilities across diverse natural language tasks; yet their ability to solve abstraction and optimization problems with const…
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories
Pantelis Dogoulis, Fabien Bernier, Félix Fourreau +2
Many real-world machine learning tasks require outputs that satisfy hard constraints, such as physical conservation laws, structured dependencies in graphs, or column-level relatio…
KCLNet: Physics-Informed Power Flow Prediction via Constraints Projections
Pantelis Dogoulis, Karim Tit, Maxime Cordy
In the modern context of power systems, rapid, scalable, and physically plausible power flow predictions are essential for ensuring the grid's safe and efficient operation. While t…