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.

翻译：高分辨率仿真模型对于表征复杂物理系统至关重要，但其高昂的计算成本严重限制了可行的高保真度评估次数。这一问题通常通过多保真度框架来解决，该框架采用具有不同保真度和评估代价的仿真器层级结构。在此场景中，整合来自这些异构源的信息以精确逼近高保真度仿真器是一项关键难题。本文提出了一种基于集成学习的多保真度仿真建模新方法。集成中的基学习器为层级克里金仿真器，能系统性地将低保真度模型信息融入高保真度预测中。通过贝叶斯模型平均聚合这些基学习器，可获得具有原则性不确定性量化的多保真度仿真器。随后，该不确定性的模型间方差分量被用作自适应设计策略中的采集准则，以利用信息性样本丰富训练集。该方法在一组公认的基准问题上进行了预测性能评估。结果表明，所提多保真度仿真器在准确性和鲁棒性方面优于单模型替代方案。此外，自适应设计策略能有效识别信息性样本，并在有限计算预算下提升仿真器性能。