Multifidelity regression of sparse plasma transport data available in disparate physical regimes

Abstract

Physical data are typically generated by experiments and computations in limited parameter regimes. When datasets generated using such disparate methods are combined into one dataset, the resulting dataset is typically sparse, with dense "islands" in a potentially high-dimensional parameter space, and predictions must be interpolated among such islands. Using plasma transport data as our example, we propose a multifidelity Gaussian-process regression framework that incorporates physical data from multiple sources at multiple fidelities. The impact of the proposed framework varies from little improvement over simpler approaches to qualitatively changing the prediction with consistently increased confidence in regions lacking high-fidelity data. By varying low- and high-fidelity data sources, we demonstrate an approach for determining when multifidelity Gaussian-process regression adds value over single-fidelity regression and therefore when its additional computational costs are merited. We also examine the case in which the outputs of the low- and high-fidelity models correspond to different physical quantities, one of which may be intrinsically computationally cheaper to compute. We conclude by analyzing strategies for sampling high-fidelity data for use in this framework, and we develop a simple sampling approach for reducing regression error across large gaps in data.