OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle
Year: 2021
Type: article
Abstract: Machine learning is becoming an increasingly popular method for building mathematical models from observations of natural processes. Here, the central challenge is to impart structure into the model parameterization to enforce physical relevance, yet retain a degree of generality so that a large variety of dynamics can be learned. We introduce a novel methodology, based on a data-driven extension of the classical Onsager principle, that strikes a balance between these competing aspects. We demonstrate its efficacy by learning quantitatively accurate and qualitatively faithful reduced order models of the Rayleigh-B\'enard convection equations.
Source: Physical Review Fluids
Authors: Haijun Yu, Xinyuan Tian, Weinan E, Qianxiao Li
Institutions: Chinese Academy of Sciences, National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences, Princeton University, Agency for Science, Technology and Research, National University of Singapore, Institute of High Performance Computing
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: 面向中高维偏微分方程的高效谱方法, G8501901902
OpenAlex ID: W3083764085