$α$-divergence Improves the Entropy Production Estimation via Machine Learning
This work addresses a specific bottleneck in nonequilibrium statistical physics for researchers, offering an incremental improvement over prior methods.
The paper tackles the problem of estimating stochastic entropy production from trajectory data by introducing a family of loss functions based on α-divergence, showing that α-NEEP with α between -1 and 0, especially α=-0.5, yields more robust performance against strong nonequilibrium driving or slow dynamics compared to existing methods, with concrete improvements in accuracy.
Recent years have seen a surge of interest in the algorithmic estimation of stochastic entropy production (EP) from trajectory data via machine learning. A crucial element of such algorithms is the identification of a loss function whose minimization guarantees the accurate EP estimation. In this study, we show that there exists a host of loss functions, namely those implementing a variational representation of the $α$-divergence, which can be used for the EP estimation. By fixing $α$ to a value between $-1$ and $0$, the $α$-NEEP (Neural Estimator for Entropy Production) exhibits a much more robust performance against strong nonequilibrium driving or slow dynamics, which adversely affects the existing method based on the Kullback-Leibler divergence ($α= 0$). In particular, the choice of $α= -0.5$ tends to yield the optimal results. To corroborate our findings, we present an exactly solvable simplification of the EP estimation problem, whose loss function landscape and stochastic properties give deeper intuition into the robustness of the $α$-NEEP.