Speaker
Lorenzo Colantonio
(Sapienza Università di Roma)
Description
We introduce an innovative approach to combinatorial optimization problems through Physics-Informed Graph Neural Networks (GNNs). We combine the structural advantages of GNNs with physics-based algorithms, enhancing solution accuracy and computational efficiency. With respect to available literature we were able to design and train a deep graph neural network model able to solve the graph colouring problem in an unsupervised way. Our method shows promising results, demonstrating the potential of merging domain-specific knowledge with machine learning, and opening possibile pathways in computational optimization problems of interest in both theoretical and experimental fundamental physics.
Primary authors
Andrea Cacioppo
(Sapienza Università di Roma and INFN Roma)
Federico Scarpati
(Sapienza Università di Roma)
Lorenzo Colantonio
(Sapienza Università di Roma)
Stefano Giagu
(Sapienza Università di Roma and INFN Roma, Roma, Italy)
