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Learning Entropy Production via Neural Networks
Dong-Kyum Kim1, Youngkyoung Bae1, Sangyun Lee1
1Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.
Physical Review Letters
|October 16, 2020
Summary
This study introduces a neural estimator for entropy production (NEEP) to calculate entropy production (EP) from system data. NEEP works without needing full system dynamics, offering a novel approach for complex systems.
Area of Science:
- Statistical physics
- Machine learning
- Computational science
Background:
- Entropy production (EP) is a fundamental concept in non-equilibrium thermodynamics.
- Estimating EP often requires detailed knowledge of system dynamics, which is not always available.
- Current methods may struggle with high-dimensional data or systems with unobservable states.
Purpose of the Study:
- To develop a novel neural estimator for entropy production (NEEP).
- To estimate EP from observed trajectories without requiring complete system dynamics.
- To validate the NEEP's applicability to complex and high-dimensional systems.
Main Methods:
- Utilized deep neural networks to construct the NEEP.
- Developed and optimized a new objective function for the estimator.
- Verified the NEEP using stochastic processes from bead spring and discrete flashing ratchet models.
- Applied the method to high-dimensional datasets and Markov systems with unobservable states.
Main Results:
- The NEEP rigorously estimates stochastic EP in steady-state systems.
- The estimator demonstrates accuracy across different stochastic models.
- The method successfully handles high-dimensional data.
- Coarse-grained EP estimation is achievable for systems with hidden states.
Conclusions:
- NEEP provides a powerful, data-driven approach for estimating entropy production.
- This method broadens the applicability of EP calculations to complex, partially observable systems.
- NEEP represents a significant advancement in applying machine learning to physical systems.
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