Bifidelity Karhunen-Lo\`eve Expansion Surrogate with Active Learning for Random Fields

TL;DR

This paper introduces a bifidelity surrogate modeling approach combining Karhunen-Loève expansion and active learning to efficiently and accurately approximate random fields under uncertainty, demonstrated on complex fluid dynamics problems.

Contribution

The paper develops a novel bifidelity surrogate framework integrating KLE, polynomial chaos, and active learning for improved efficiency in modeling uncertain fields.

Findings

Achieves higher accuracy with fewer high-fidelity samples.

Demonstrates effectiveness on complex fluid flow simulations.

Outperforms single-fidelity and random sampling methods.

Abstract

We present a bifidelity Karhunen-Lo\`eve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs. The approach combines the spectral efficiency of the KLE with polynomial chaos expansions (PCEs) to preserve an explicit mapping between input uncertainties and output fields. By coupling inexpensive low-fidelity (LF) simulations that capture dominant response trends with a limited number of high-fidelity (HF) simulations that correct for systematic bias, the proposed method enables accurate and computationally affordable surrogate construction. To further improve surrogate accuracy, we form an active learning strategy that adaptively selects new HF evaluations based on the surrogate's generalization error, estimated via cross-validation and modeled using Gaussian process regression. New HF samples are then acquired by maximizing an expected improvement criterion, targeting regions of high surrogate error. The resulting BF-KLE-AL framework is demonstrated on three examples of increasing complexity: a one-dimensional analytical benchmark, a two-dimensional convection-diffusion system, and a three-dimensional turbulent round jet simulation based on Reynolds-averaged Navier--Stokes (RANS) and enhanced delayed detached-eddy simulations (EDDES). Across these cases, the method achieves consistent improvements in predictive accuracy and sample efficiency relative to single-fidelity and random-sampling approaches.