超微分敏感性分析：关于模型偏差的敏感性分析：顺序最优实验设计
Hyper-differential sensitivity analysis with respect to model discrepancy: Sequential optimal experimental design
AI总结：
本文提出一种基于超微分敏感性分析的方法，利用有限高保真模拟更新优化解，通过伪时间延续技术提高计算效率，并提出贝叶斯框架指导数据采集以减少模型偏差不确定性。
摘要：
大规模优化问题在物理科学中普遍存在；然而，高保真模型往往复杂且计算上不可行。一种实用的替代方法是使用低保真模型来促进优化。然而，高保真与低保真模型之间的差异可能导致次优解。为了解决这个问题，我们基于最近的超微分敏感性分析工作，利用有限的高保真模拟来更新优化解。本文的贡献包括：(i) 融入伪时间延续技术以高效计算更高精度的最优解更新；(ii) 提出一种贝叶斯框架用于顺序数据采集，战略性地指导高保真评估并减少模型偏差估计的不确定性。数值结果表明，我们的框架仅通过少量高保真评估就显著提高了优化解的质量。
英文摘要：
Large-scale optimization problems are ubiquitous in the physical sciences; yet, high-fidelity models can often be complex and computationally prohibitive for optimization. A practical alternative is to use a low-fidelity model to facilitate optimization. However, the discrepancy between the high- and low-fidelity models can lead to suboptimal solutions. To address this, we build on recent work in Hyper-Differential Sensitivity Analysis to leverage limited high-fidelity simulations to update the optimization solution. Our contributions in this article include: (i) incorporating pseudo-time continuation techniques to efficiently compute higher-accuracy optimal solution updates, and (ii) proposing a Bayesian framework for sequential data acquisition that strategically guides high-fidelity evaluations and reduces uncertainty in the model discrepancy estimation. Numerical results demonstrate that our framework delivers significant improvements to optimization solutions with only a few high-fidelity evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
