Machine-learning iterative calculation of entropy for physical systems
Abstract
The calculation of entropy of a physical system is a fundamental step in learning its thermodynamic behavior. However, current methods to compute the entropy are often system specific and computationally costly. Here, we propose a method that is efficient, accurate, and general for computing the entropy of arbitrary physical systems. Our method is based on computing the mutual information between subsystems within the studied system, using a convolutional neural network. This iterative procedure provides accurate entropy evaluation for systems in and out of equilibrium.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 19, 2020
- Source ID
- 10.1073/pnas.2017042117
Entities
People
- Amit Nir
- Eran Sela
- Roy Beck-Barkai
- Yohai Bar-Sinai
Organizations
- Army Research Office
- Google Research
- Israel Science Foundation
- Tel Aviv University
- United States – Israel Binational Science Foundation