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Physics-informed dynamical system models form critical components of digitaltwins of the built environment. These digital twins enable the design ofenergy-efficient infrastructure, but must be properly calibrated to accuratelyreflect system behavior for downstream prediction and analysis. Dynamicalsystem models of modern buildings are typically described by a large number ofparameters and incur significant computational expenditure during simulations.To handle large-scale calibration of digital twins without exorbitantsimulations, we propose ANP-BBO: a scalable and parallelizable batch-wiseBayesian optimization (BBO) methodology that leverages attentive neuralprocesses (ANPs).

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