TR2023-138
Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States
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- "Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States", IEEE Control Systems Letters, DOI: 10.1109/LCSYS.2023.3334959, November 2023.BibTeX TR2023-138 PDF
- @article{Deshpande2023nov,
- author = {Deshpande, Vedang M. and Chakrabarty, Ankush and Vinod, Abraham P. and Laughman, Christopher R.},
- title = {Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States},
- journal = {IEEE Control Systems Letters},
- year = 2023,
- month = nov,
- doi = {10.1109/LCSYS.2023.3334959},
- url = {https://www.merl.com/publications/TR2023-138}
- }
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- "Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States", IEEE Control Systems Letters, DOI: 10.1109/LCSYS.2023.3334959, November 2023.
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MERL Contacts:
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Research Areas:
Abstract:
Physics-based computational models of vapor compression systems (VCSs) enable high-fidelity simulations but require high-dimensional state representations. The underlying VCS dynamics are stiff, constrained by conservation laws, and only a small fraction of states can be measured. While recent advances on constrained extended Kalman filtering (EKF) have provided a systematic framework for estimating VCS states via simulation models, two major bottlenecks to efficient implementation include: (i) expensive forward predictions requiring customized stiff solvers; and, (ii) frequent and computation- ally expensive linearization operations on high-dimensional nonlinear models. In this paper, we circumvent these bottlenecks by constructing deep autoencoder (AE)-based state-space models (SSMs) from simulation data for which both forward predictions and linearization operations via automatic differentiation can be performed efficiently. In addition, we incorporate physical constraints based on pressure gradients explicitly into the autoencoder, and demonstrate, on a Julia-based high-fidelity simulator, that the physics-constrained model improves the estimation performance compared to a AE-based SSM that does not enforce physics.
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.
The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
- 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.