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Inferring Entropy Production in Many-Body Systems Using Nonequilibrium Maximum Entropy
Miguel Aguilera1,2, Sosuke Ito3,4, Artemy Kolchinsky3,5
1BCAM-Basque Center for Applied Mathematics, 48009 Bilbao, Spain.
Physical Review Letters
|March 6, 2026
Summary
We developed a new method to calculate entropy production in complex systems. This approach overcomes computational limits and works for systems with long memory, offering insights into their thermodynamics.
Area of Science:
- Statistical physics
- Computational thermodynamics
- Complex systems analysis
Background:
- Estimating entropy production (EP) in high-dimensional stochastic systems is computationally challenging.
- Standard methods fail for many-body and non-Markovian systems due to limitations.
Purpose of the Study:
- To propose a novel method for inferring entropy production in complex systems.
- To overcome limitations of existing techniques for high-dimensional and non-Markovian systems.
Main Methods:
- Exploiting a nonequilibrium maximum entropy principle analogue and convex duality.
- Inferring trajectory-level EP and average EP bounds using trajectory observables.
- Avoiding reconstruction of probability distributions or rate matrices.
Main Results:
- Successfully inferred entropy production in a 1000-spin disordered model and neural spike-train data.
- Developed a method applicable to systems with long memory and without special assumptions.
- Enabled hierarchical decomposition of EP and provided a thermodynamic uncertainty relation interpretation.
Conclusions:
- The proposed method offers a tractable approach for calculating entropy production in previously intractable systems.
- This technique provides new tools for analyzing thermodynamics in complex, nonequilibrium systems.
- The method is versatile, applicable to diverse systems from physics to neuroscience.
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