Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Abstract
Estimating the free energy, as well as other thermodynamic observables, is a key task in lattice field theories. Recently, it has been pointed out that deep generative models can be used in this context [1]. Crucially, these models allow for the direct estimation of the free energy at a given point in parameter space. This is in contrast to existing methods based on Markov chains which generically require integration through parameter space. In this contribution, we will review this novel machine-learning-based estimation method. We will in detail discuss the issue of mode collapse and outline mitigation techniques which are particularly suited for applications at finite temperature.
Keywords
Cite
@article{arxiv.2111.11303,
title = {Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse},
author = {Kim A. Nicoli and Christopher Anders and Lena Funcke and Tobias Hartung and Karl Jansen and Pan Kessel and Shinichi Nakajima and Paolo Stornati},
journal= {arXiv preprint arXiv:2111.11303},
year = {2022}
}
Comments
10 pages, 2 figures, Proceedings of the 38th International Symposium on Lattice Field Theory, 26th-30th July 2021, Zoom/Gather@Massachusetts Institute of Technology