Multifidelity uncertainty propagation combines the efficiency of low-fidelity models with the accuracy of a high-fidelity model to construct statistical estimators of quantities of interest. It is well known that the effectiveness of such methods depends crucially on the relative correlations and computational costs of the available computational models. However, the question of how to automatically tune low-fidelity models to maximize performance remains an open area of research. This work investigates automated model tuning, which optimizes model hyperparameters to minimize estimator variance within a target computational budget. Focusing on multifidelity trajectory simulation estimators, the cost-versus-precision tradeoff enabled by this approach is demonstrated in a practical, online setting where upfront tuning costs cannot be amortized. Using a real-world entry, descent, and landing example, it is shown that automated model tuning largely outperforms hand-tuned models even when the overall computational budget is relatively low. Furthermore, for scenarios where the computational budget is large, model tuning solutions can approach the best-case multifidelity estimator performance where optimal model hyperparameters are known a priori. Recommendations for applying model tuning in practice are provided and avenues for enabling adoption of such approaches for budget-constrained problems are highlighted.

翻译：多保真度不确定性传播方法通过结合低保真度模型的高效性与高保真度模型的精确性，构建目标统计量的估计器。众所周知，此类方法的有效性主要取决于可用计算模型之间的相对相关性及计算成本。然而，如何通过自动调优低保真度模型以实现性能最大化，仍是亟待解决的研究课题。本研究探讨自动化模型调优方法，该方法通过优化模型超参数以最小化目标计算预算内的估计器方差。聚焦于多保真度轨迹仿真估计器，本研究在实际在线场景中展示了该方法实现的成本-精度权衡，其中前期调优成本无法被分摊。通过真实世界进入、下降与着陆案例的验证，研究表明即使总体计算预算相对较低，自动化模型调优仍显著优于人工调优模型。此外，在计算预算充足的情况下，模型调优方案能够逼近已知最优模型超参数时的最佳多保真度估计器性能。本文提供了实际应用模型调优的建议，并强调了在预算受限问题中推广此类方法的关键路径。