Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, these high-fidelity models are by themselves insufficient for optimal experimental design, because their computational cost remains too high to efficiently and effectively explore the numerous parameters required to describe a typical experiment. Traditionally, ICF design has relied on low-fidelity modeling to initially identify potentially interesting design regions, which are then subsequently explored via selected high-fidelity modeling. In this paper, we demonstrate that this two-step approach can be insufficient: even for simple design problems, a two-step optimization strategy can lead high-fidelity searching towards incorrect regions and consequently waste computational resources on parameter regimes far away from the true optimal solution. We reveal that a primary cause of this behavior in ICF design problems is the presence of low-fidelity optima in distinct regions of the parameter space from high-fidelity optima. To address this issue, we propose an iterative multifidelity Bayesian optimization method based on Gaussian Process Regression that leverages both low- and high-fidelity modelings. We demonstrate, using both two- and eight-dimensional ICF test problems, that our algorithm can effectively utilize low-fidelity modeling for exploration, while automatically refining promising designs with high-fidelity models. This approach proves to be more efficient than relying solely on high-fidelity modeling for optimization.

翻译：由于成本高昂，惯性约束聚变（ICF）实验严重依赖数值模拟来指导设计。随着模拟技术的进步，用于规划新实验的模型保真度也随之提高。然而，这些高保真模型本身不足以实现最优实验设计，因为其计算成本仍然过高，无法高效且有效地探索描述典型实验所需的众多参数。传统上，ICF设计依赖低保真建模来初步识别潜在感兴趣的设计区域，随后通过选择的高保真建模对这些区域进行深入探索。本文中，我们证明这种两步法可能存在不足：即使是简单的设计问题，两步优化策略也可能导致高保真搜索指向错误区域，从而浪费计算资源于远离真正最优解的参数空间。我们揭示，ICF设计问题中这一行为的主要原因是低保真最优解与高保真最优解存在于参数空间的不同区域。为解决此问题，我们提出一种基于高斯过程回归的迭代式多保真度贝叶斯优化方法，该方法同时利用低保真和高保真建模。通过使用二维和八维的ICF测试问题，我们证明该算法能够有效利用低保真建模进行探索，同时自动使用高保真模型精炼有前景的设计。实验证明，此方法比单纯依赖高保真建模进行优化更为高效。