Abstract

Complex dissipative systems appear across science and engineering, from polymers and active matter to learning algorithms. These systems operate far from equilibrium, where energy dissipation and time irreversibility govern their behavior but are difficult to quantify from data. In this work, we introduce a universal and identifiable neural framework that learns dissipative stochastic dynamics while ensuring interpretability, expressiveness, and uniqueness. Our method identifies a unique energy landscape, separates reversible from irreversible motion, and allows direct computation of the entropy production, providing a principled measure of irreversibility and deviations from equilibrium. Applications to polymer stretching in elongational flow and to SGLD reveal new insights, including super-linear scaling of barrier heights and sub-linear scaling of entropy production rates with the strain rate, and the suppression of irreversibility with increasing batch size. We will also introduce relevant algorithms and analysis for learning dynamical systems.

WebEx Link

https://www.google.com/url?q=https://nationaltaiwanuniversity-ksz.my.webex.com/nationaltaiwanuniversity-ksz.my/j.php?MTID%3Dm055b3fee8f403cafeb99fbffa22f4e0a&sa=D&source=calendar&ust=1789262607315121&usg=AOvVaw3WeXOkJod4QVosa3AObvwt

Organizer

Te-Sheng Lin (NYCU)