Learning entropy production via neural networks
This provides a method for physicists and researchers to analyze entropy production in complex systems, but it is incremental as it builds on existing neural network approaches.
The authors tackled the problem of estimating entropy production from system trajectories without detailed dynamics knowledge, and demonstrated their neural estimator's effectiveness on stochastic processes and high-dimensional data.
This Letter presents a neural estimator for entropy production, or 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 verify 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.