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arXiv · 2505.10444

Inferring entropy production in many-body systems using nonequilibrium maximum entropy

Miguel Aguilera·Sosuke Ito·Artemy Kolchinsky
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Abstract

We propose a method for inferring entropy production (EP) in high-dimensional stochastic systems, including many-body systems and non-Markovian systems with long memory. Standard techniques for estimating EP become intractable in such systems due to computational and statistical limitations. We infer trajectory-level EP and lower bounds on average EP by exploiting a nonequilibrium analogue of the Maximum Entropy principle, along with convex duality. Our approach uses only samples of trajectory observables, such as spatiotemporal correlations. It does not require reconstruction of high-dimensional probability distributions or rate matrices, nor impose any special assumptions such as discrete states or multipartite dynamics. In addition, it may be used to compute a hierarchical decomposition of EP, reflecting contributions from different interaction orders, and it has an intuitive physical interpretation as a "thermodynamic uncertainty relation." We demonstrate its numerical performance on a disordered nonequilibrium spin model with 1000 spins and a large neural spike-train dataset.

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Miguel Aguilera, Sosuke Ito, Artemy Kolchinsky. 2025-05-15. Inferring entropy production in many-body systems using nonequilibrium maximum entropy. https://doi.org/10.1103/xgkj-dxzh

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