Explaining the Machine Learning Solution of the Ising Model

arXiv:2402.11701v1 Announce Type: cross
Abstract: As powerful as machine learning (ML) techniques are in solving problems involving data with large dimensionality, explaining the results from the fitted parameters remains a challenging task of utmost importance, especially in physics applications. Here it is shown how this can be accomplished for the ferromagnetic Ising model, the target of many ML studies in the last years. By using a neural network (NN) without any hidden layers and the symmetry of the Hamiltonian to …

abstract applications arxiv cond-mat.dis-nn cs.lg data dimensionality importance machine machine learning parameters physics physics.comp-ph solution type