Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins

• URL: http://arxiv.org/abs/2106.15502v1
• Date: Tue, 29 Jun 2021 15:30:55 GMT
• Title: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
• Authors: Ankush Chakrabarty, Gordon Wichern, Christopher Laughman
• Abstract summary: Physics-informed dynamical system models form critical components of digital twins of the built environment. We propose ANP-BBO: a scalable and parallelizable batch-wise Bayesian optimization (BBO) methodology.
• Score: 10.555398506346291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 large-scale 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).

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