🤖 AI Summary
This work addresses the challenge of high computational cost associated with high-fidelity simulations, which hinders extensive evaluation. To overcome this limitation, the authors propose a multi-fidelity surrogate modeling framework that integrates data of varying fidelity levels. The approach employs an ensemble of hierarchical Kriging models as base learners, whose predictions are combined via Bayesian model averaging. Crucially, the method introduces an innovative uncertainty quantification mechanism based on inter-model variance, which informs an adaptive sampling strategy to optimize the selection of training samples. Evaluated on multiple benchmark problems, the proposed framework consistently outperforms single-model approaches, achieving superior prediction accuracy, enhanced robustness, and improved data efficiency under constrained computational budgets.
📝 Abstract
High-resolution simulation models are essential for representing complex physical systems, yet their substantial computational cost severely limits the number of feasible high-fidelity (HF) evaluations. This problem is often addressed through multi-fidelity frameworks, which employ hierarchies of simulators with varying levels of fidelity and evaluation cost. A key difficulty in this setting is integrating information from such heterogeneous sources to accurately approximate HF simulators.This paper proposes a novel multi-fidelity emulation methodology based on ensemble learning. The base learners of the ensemble are hierarchical kriging emulators that systematically incorporate information from lower-fidelity models into HF predictions. Aggregation of these base learners via Bayesian model averaging yields the multi-fidelity emulator with principled uncertainty quantification. The between-model variance component of this uncertainty is then employed as the acquisition criterion in an adaptive design strategy to enrich the training set with informative samples. The predictive performance of the approach is assessed on a collection of well-established benchmark problems. Results show that our multi-fidelity emulator outperforms single-model alternatives in terms of accuracy and robustness. Furthermore, the adaptive design strategy effectively identifies informative samples and improves emulator performance under limited computational budgets.