Constructing custom thermodynamics using deep learning
Li Qianxiao, National University of Singapore
https://blog.nus.edu.sg/qianxiaoli/
Kedar Hippalgaonkar, Nayang Technological University
https://dr.ntu.edu.sg/cris/rp/rp00405
Abstract:
We discuss some recent work on constructing stable and interpretable macroscopic thermodynamics from trajectory data using deep learning. We develop a modelling approach: instead of generic neural networks as functional approximators, we use a model-based ansatz for the dynamics following a suitable generalisation of the classical Onsager principle for non-equilibrium systems. This allows the construction of macroscopic dynamics that are physically motivated and can be readily used for subsequent analysis and control. We discuss applications in the analysis of polymer stretching in elongational flow.
Bio:
Qianxiao Li is an assistant professor in the Department of Mathematics, and a principal investigator in the Institute for Functional Intelligent Materials, National University of Singapore. He graduated with a BA in mathematics from the University of Cambridge and a PhD in applied mathematics from Princeton University. His research interests include the interplay of machine learning and dynamical systems, control theory, stochastic optimisation algorithms and data-driven methods for science and engineering.
#modeling #simulation #sciml #science #ml #ai #physics #dynamics