Deep learning probability flows and entropy production rates in active matter.

ABSTRACT: Active matter systems, from self-propelled colloids to motile bacteria, are characterized by the conversion of free energy into useful work at the microscopic scale. They involve physics beyond the reach of equilibrium statistical mechanics, and a persistent challenge has been to understand the nature of their nonequilibrium states. The entropy production rate and the probability current provide quantitative ways to do so by measuring the breakdown of time-reversal symmetry. Yet, their efficient computation has remained elusive, as they depend on the system's unknown and high-dimensional probability density. Here, building upon recent advances in generative modeling, we develop a deep learning framework to estimate the score of this density. We show that the score, together with the micros

SUBMITTER: Boffi NM 

PROVIDER: S-EPMC11194503 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature