Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse

• URL: http://arxiv.org/abs/2111.11303v1
• Date: Mon, 22 Nov 2021 15:59:08 GMT
• Title: Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
• Authors: Kim A. Nicoli, Christopher Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
• Abstract summary: Deep generative models allow for the direct estimation of the free energy at a given point in parameter space. In this contribution, we will review this novel machine-learning-based estimation method.
• Score: 5.096726017663865
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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