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[2505.10444] Inferring entropy production in many-body systems using nonequilibrium maximum entropy
Summary
This article presents a novel method for inferring entropy production in many-body systems using a nonequilibrium maximum entropy approach, addressing limitations of traditional techniques.
Why It Matters
Understanding entropy production in complex systems is crucial for advancements in statistical mechanics and machine learning. This research provides a new methodology that can enhance the analysis of high-dimensional stochastic systems, potentially impacting various fields including physics and neuroscience.
Key Takeaways
- Introduces a method for inferring entropy production in many-body systems.
- Overcomes computational and statistical limitations of existing techniques.
- Utilizes trajectory observables without requiring high-dimensional probability distributions.
- Offers a hierarchical decomposition of entropy production contributions.
- Demonstrates effectiveness through numerical performance on complex models.
Condensed Matter > Statistical Mechanics arXiv:2505.10444 (cond-mat) [Submitted on 15 May 2025 (v1), last revised 19 Feb 2026 (this version, v4)] Title:Inferring entropy production in many-body systems using nonequilibrium maximum entropy Authors:Miguel Aguilera, Sosuke Ito, Artemy Kolchinsky View a PDF of the paper titled Inferring entropy production in many-body systems using nonequilibrium maximum entropy, by Miguel Aguilera and 2 other authors View PDF HTML (experimental) 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 di...