TR2021-086
ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
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- "ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins", International Conference on Machine Learning (ICML), July 2021.BibTeX TR2021-086 PDF
- @inproceedings{Chakrabarty2021jul,
- author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
- title = {ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins},
- booktitle = {International Conference on Machine Learning (ICML)},
- year = 2021,
- month = jul,
- url = {https://www.merl.com/publications/TR2021-086}
- }
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- "ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins", International Conference on Machine Learning (ICML), July 2021.
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MERL Contacts:
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Research Areas:
Abstract:
Physics-informed dynamical system models form critical components of digital twins of the built environment. These digital twins enable the design of energy-efficient infrastructure, but must be properly calibrated to accurately reflect system behavior for downstream prediction and analysis. Dynamical system models of modern buildings are typically described by a large number of parameters and incur significant computational expenditure during simulations. To handle largescale calibration of digital twins without exorbitant simulations, we propose ANP-BBO: a scalable and parallelizable batch-wise Bayesian optimization (BBO) methodology that leverages attentive neural processes (ANPs).
Related News & Events
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NEWS Ankush Chakrabarty gave an invited talk at CRAN: Centre de Recherche en Automatique de Nancy, France Date: October 21, 2021
Where: Université de Lorraine, France
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty (RS, Multiphysical Systems Team) gave an invited talk on `Bayesian-Optimized Estimation and Control for Buildings and HVAC' at the Research Center for Automatic Control (CRAN) in the University of Lorraine in France. The talk presented recent MERL research on probabilistic machine learning for set-point optimization and calibration of digital twins for building energy systems.
Related Publication
BibTeX arXiv
- @article{Chakrabarty2021jun,
- author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
- title = {ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins},
- journal = {arXiv},
- year = 2021,
- month = jun,
- url = {https://arxiv.org/abs/2106.15502}
- }