Inferring entropy production in many-body systems using nonequilibrium maximum entropy
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918344202911744 |
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| author | Aguilera, Miguel Ito, Sosuke Kolchinsky, Artemy |
| author_facet | Aguilera, Miguel Ito, Sosuke Kolchinsky, Artemy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring entropy production in many-body systems using nonequilibrium maximum entropy Aguilera, Miguel Ito, Sosuke Kolchinsky, Artemy Statistical Mechanics Machine Learning Adaptation and Self-Organizing Systems Neurons and Cognition 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. |
| title | Inferring entropy production in many-body systems using nonequilibrium maximum entropy |
| topic | Statistical Mechanics Machine Learning Adaptation and Self-Organizing Systems Neurons and Cognition |
| url | https://arxiv.org/abs/2505.10444 |