TR2021-086

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


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 Publication

  •  Chakrabarty, A., Wichern, G., Laughman, C.R., "ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins", arXiv, June 2021.
    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}
    • }