Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse

TL;DR

This paper reviews a machine learning approach for estimating thermodynamic observables like free energy in lattice field theories, addressing mode collapse issues and proposing mitigation techniques for finite temperature applications.

Contribution

It introduces a novel machine learning-based method for direct free energy estimation and discusses strategies to mitigate mode collapse in this context.

Findings

Machine learning models enable direct free energy estimation.

Mode collapse poses challenges in thermodynamic observable estimation.

Mitigation techniques improve model reliability at finite temperature.

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.