活动时间：2026-05-13 16:00
活动地点：2号学院楼2432会议室
主讲人：肖骞
主讲人简介：
肖骞，上海交通大学数学科学学院统计系长聘副教授、数据智能研究中心主任。2017年于美国加州大学洛杉矶分校（UCLA）获得统计学博士学位，曾在美国佐治亚大学统计系任助理教授及长聘副教授。其主要研究方向包括：最优计算机试验设计与分析和不确定性量化。其研究成果发表于《Annals of Statistics》《Journal of the American Statistical Association》《Biometrika》《Technometrics》等统计学顶级期刊。主持国家重点研发计划青年科学家项目，国家级青年人才计划，小米青年学者。
内容摘要：
Accurate prediction of satellite trajectories in low Earth orbit requires reliable quantification of atmospheric drag, yet high-fidelity drag simulators are too computationally expensive for routine large-scale uncertainty quantification. In the GRACE satellite-drag application considered here, each simulator run is costly, and reliable screening of physical inputs is needed before constructing surrogate models or performing broader uncertainty propagation. This case study exposes a practical limitation of the original Maximum One-Factor-At-A-Time (MOFAT) design: although MOFAT is economical for one-shot factor screening, it does not provide a principled way to augment the design sequentially when the initial run budget is insufficient. We address this problem by developing two batch-sequential extensions of MOFAT: the Interpolation MOFAT (I-MOFAT) and Sliced MOFAT (S-MOFAT) designs. Both preserve the structure required for estimating total Sobol' indices while allowing the design to grow in stages. I-MOFAT supports local refinement through intermediate levels, whereas S-MOFAT promotes broader exploration through a sliced construction. Numerical studies and the GRACE case study show that these designs identify influential inputs more reliably than competing screening methods under constrained computational budgets.
主持人：戚良玮