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Activity Number: 309 - Interface Between Machine Learning and Uncertainty Quantification
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Uncertainty Quantification in Complex Systems Interest Group
Abstract #309663
Title: On-Site Surrogates for Large-Scale Calibration
Author(s): Jiangeng Huang* and Robert Gramacy
Companies: University of California Santa Cruz and Virginia Tech
Keywords: Uncertainty quantification; Model calibration; Bayesian computation; Hierarchical model; Big data

Motivated by a computer model calibration problem from the oil and gas industry, involving the design of a honeycomb seal, we develop a new Bayesian methodology to cope with limitations in the canonical apparatus stemming from several factors. We propose a new strategy of on-site design and surrogate modeling for a computer simulator acting on a high-dimensional input space that, although relatively speedy, is prone to numerical instabilities, missing data, and nonstationary dynamics. Our aim is to strike a balance between data-faithful modeling and computational tractability in a calibration framework--tailoring the computer model to a limited field experiment. Situating our on-site surrogates within the canonical calibration apparatus requires updates to that framework. We describe a novel yet intuitive Bayesian setup that carefully decomposes otherwise prohibitively large matrices by exploiting the sparse blockwise structure. Empirical illustrations demonstrate that this approach performs well on toy data and our motivating honeycomb example.

Authors who are presenting talks have a * after their name.

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