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Constructing custom thermodynamics using deep learning
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DOI：10.1038/s43588-023-00581-5.png)
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Abstract
En 中文
One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications. The authors develop a general method that combines machine learning and physics to construct macroscopic dynamics directly from microscopic observations, leading to an intuitive understanding of polymer stretching in elongational flow.
Keywords:
PROFESSIONAL SPORTS
FOOTBALL CLUBS
BROWNIAN DYNAMICS
NEURAL-NETWORKS
IRREVERSIBLE-PROCESSES
FINANCIAL PERFORMANCE
DISCRIMINANT-ANALYSIS
UTILITY MAXIMIZATION
RECIPROCAL RELATIONS
COMPETITIVE BALANCE
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