报告题目：Fast and Scalable Computational Methods for Learning and Optimization Under Uncertainty
报告时间：2021年4月28日，星期三，上午11:00-12:00
腾讯会议ID：596 856 329
报告人： 陈鹏 教授，美国得克斯大学奥斯汀分校
报告摘要：
In this talk, I will present some recent work on fast and scalable computational methods for surrogate modeling, statistical learning, experimental design, and stochastic optimization under uncertainty. Tremendous computational challenges are faced for such problems when (1) the model (e.g., large-scale PDE) is expensive to solve; (2) the design/control/uncertain variables are high-dimensional, etc. We tackle these challenges by exploiting the data and system informed properties including smoothness, intrinsic low-dimensionality, and low-rankness. I will briefly present a few methods including model reduction, randomized tensor decomposition, derivative informed deep learning, projected variational inference, and functional Taylor approximations. Time permits, I will also briefly touch on some applications including underground water management, directed self-assembly for semiconductor manufacturing, design of acoustic and electromagnetic metamaterials, gravitational wave detection from black hole collision, turbulent combustion, stellarator design for plasma fusion, and COVID-19.
报告人简介：
Peng Chen was an undergraduate student in the School of Mathematics and Statistics at XJTU from 2005 to 2009. He obtained his Ph.D. degree in computational mathematics from EPFL, Switzerland, in 2014, and worked as a postdoc researcher and a lecturer for a year (2014-2015) at ETH Zurich, Switzerland. He moved to Oden Institute at UT Austin, US, and has been working as a long-term researcher since then. His research interests include uncertainty quantification, Bayesian inference, experimental design, stochastic optimization, and machine learning in various computational science and engineering fields.
腾讯会议信息：
会议主题：贾骏雄预定的会议
会议时间：2021/04/28 10:00-12:30 (GMT+08:00) 中国标准时间 - 北京
点击链接入会，或添加至会议列表：
https://meeting.tencent.com/s/jRPoJxiTIHjT
会议 ID：596 856 329
