Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modeling with Gaussian process regression

TL;DR

This paper develops an optimal data selection criterion for Gaussian process regression in reduced-order modeling, deriving an asymptotic form that ensures balanced sampling and improved model convergence.

Contribution

It introduces a novel optimality condition for data point selection in GPR-based reduced-order modeling and derives a computable asymptotic form for practical implementation.

Findings

The asymptotic criterion guarantees balanced data sampling.

The method improves convergence of the GPR model.

It provides a theoretically grounded approach for data-driven reduced-order modeling.

Abstract

We derive criteria for the selection of datapoints used for data-driven reduced-order modeling 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 for sampling towards data-driven reduced-order modeling.