Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with Gaussian process regression
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2112.02636.pdf
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Author(s)
Sapsis, Themistoklis P.
Blanchard, Antoine
Date Issued
June 20, 2022
Journal
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Publisher
The Royal Society
Citation
Sapsis, Themistoklis P. and Blanchard, Antoine. 2022. "Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with Gaussian process regression." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380 (2229).
Version
Author's final manuscript
Abstract
We derive criteria for the selection of datapoints used for data-driven reduced-order modelling and other areas of supervised learning based on Gaussian process regression (GPR). While this is a well-studied area in the fields of active learning and optimal experimental design, most criteria in the literature are empirical. Here we introduce an optimality condition for the selection of a new input defined as the minimizer of the distance between the approximated output probability density function (pdf) of the reduced-order model and the exact one. Given that the exact pdf is unknown, we define the selection criterion as the supremum over the unit sphere of the native Hilbert space for the GPR. The resulting selection criterion, however, has a form that is difficult to compute. We combine results from GPR theory and asymptotic analysis to derive a computable form of the defined optimality criterion that is valid in the limit of small predictive variance. The derived asymptotic form of the selection criterion leads to convergence of the GPR model that guarantees a balanced distribution of data resources between probable and large-deviation outputs, resulting in an effective way of sampling towards data-driven reduced-order modelling.
Subjects
General Physics and Astronomy
General Engineering
General Mathematics
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1098/rsta.2021.0197
