Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Conference · Mon Jul 03 04:00:00 EDT 2023

DOI:https://doi.org/10.23919/ACC55779.2023.10155901· OSTI ID:2008419

Nghiem, Truong ^[1]; Drgona, Jan ^[2]; Jones, Colin ^[3]; Nagy, Zoltan ^[4]; Schwan, Roland ^[3]; Dey, Biswadip ^[5]; Chakrabarty, Ankush ^[6]; Di Cairano, Stefano ^[6]; Paulson, Joel A. ^[7]; Carron, Andrea ^[8]; Zeilinger, Melanie N. ^[8]; Shaw Cortez, Wenceslao E. ^[2]; Vrabie, Draguna L. ^[2]

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1. Northern Arizona University
2. BATTELLE (PACIFIC NW LAB)
3. Ecole Polytechnique Federale de Lausanne
4. University of Texas at Austin
5. Siemens
6. Mitsubishi Electric Research Laboratories
7. Ohio State University
8. ETH Zurich

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, {PIML} models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, {PIML} models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of {PIML} is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in {PIML} for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of {PIML} models; and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2008419
• Report Number(s): PNNL-SA-183234
• Country of Publication: United States
• Language: English