TR2023-052
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
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- "Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems", American Control Conference (ACC), DOI: 10.23919/ACC55779.2023.10155901, May 2023.BibTeX TR2023-052 PDF
- @inproceedings{Ngheim2023may,
- author = {Ngheim, Truong X. and Drgona, Jan and Jones, Colin and Nagy, Zoltan and Schwan, Roland and Dey, Biswadip and Chakrabarty, Ankush and Di Cairano, Stefano and Paulson, Joel A. and Carron, Andrea and Zeilinger, Melanie and Cortez, Wenceslaw S. and Vrabie, Draguna L.},
- title = {Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems},
- booktitle = {American Control Conference (ACC)},
- year = 2023,
- month = may,
- doi = {10.23919/ACC55779.2023.10155901},
- url = {https://www.merl.com/publications/TR2023-052}
- }
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- "Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems", American Control Conference (ACC), DOI: 10.23919/ACC55779.2023.10155901, May 2023.
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MERL Contacts:
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Research Areas:
Abstract:
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.
Related News & Events
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NEWS Ankush Chakrabarty gave a lecture at UT-Austin's Seminar Series on Occupant-Centric Grid-Interactive Buildings Date: March 20, 2024
Where: Austin, TX
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
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- Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
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NEWS Ankush Chakrabarty co-organized three sessions at the ACC2023, and was nominated for Best Energy Systems Paper. Date: June 30, 2023 - June 2, 2023
Where: San Diego, CA
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Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.