【报告摘要】Computer simulations serve as powerful tools for scientists and engineers to gain insights into complex systems. Less costly than physical experiments, computer experiments sometimes involve large number of trials. Conventional design optimization and model fitting methods for computer experiments are inefficient for large-scale problems. In this paper, we propose new methods to optimize good lattice point sets, using less computation to construct designs with enhanced space-filling properties such as high separation distance, low discrepancy, and high separation distance on projections. These designs show promising performance in uncertainty quantification as well as physics-informed neural networks.

 

【报告人简介】何煦，本科毕业于北京大学数学科学学院，博士毕业于威斯康星大学麦迪逊分校统计系。2012年博士毕业后入职中国科学院数学与系统科学研究院系统科学研究所，现任副研究员。主要研究方向包括实验设计，特别是计算机仿真实验的设计及分析。在Annals of Statistics，Journal of the American Statistical Association，Biometrika，Journal of Machine Learning Research等SCI期刊发表论文十余篇，曾主持国家自然科学基金委优秀青年基金、面上项目、青年基金。