Learning Entropy Production via Neural Networks

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This Letter presents a neural estimator for entropy production (NEEP), that estimates entropy production (EP) from trajectories of relevant variables without detailed information on the system dynamics. For steady state, we rigorously prove that the estimator, which can be built up from different choices of deep neural networks, provides stochastic EP by optimizing the objective function proposed here. We vetify the NEEP with the stochastic processes of the bead spring and discrete flashing ratchet models and also demonstrate that our method is applicable to high-dimensional data and can provide coarse-grained EP for Markov systems with unobservable states.
Publisher
AMERICAN PHYSICAL SOCIETY
Issue Date
2020-10
Language
English
Article Type
Article
Citation

PHYSICAL REVIEW LETTERS, v.125, no.14, pp.140604

ISSN
0031-9007
DOI
10.1103/PhysRevLett.125.140604
URI
http://hdl.handle.net/10203/276885
Appears in Collection
PH-Journal Papers(저널논문)
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