2 papers
cs.AI2026
Scaling Multi-Agent Epistemic Planning through GNN-Derived Heuristics
Giovanni Briglia, Francesco Fabiano, Stefano Mariani
Multi-agent Epistemic Planning (MEP) is an autonomous planning framework for reasoning about both the physical world and the beliefs of agents, with applications in domains where i…
cs.LG2026
Unsupervised Physics-Informed Operator Learning through Multi-Stage Curriculum Training
Paolo Marcandelli, Natansh Mathur, Stefano Markidis +2
Solving partial differential equations remains a central challenge in scientific machine learning. Neural operators offer a promising route by learning mappings between function sp…