报告题目： Learn to Simulate: Generative Metamodeling via Quantile Regression
报 告 人：洪流 复旦大学
报告时间：2023年11月23日(周四) 10:00-11:30
报告地点：中国科学院大学中关村校区教学楼S406
腾讯会议：323-662-313
内容摘要
Stochastic simulation models that capture the dynamics of complex systems often require a significant amount of running time. They are typically not suitable for real-time decision makings. In this paper we propose a quantile-regression based generative-metamodeling approach to learn from the simulation data and to create a fast “simulator of simulator”, which can generate observations that have the (approximately) same distribution as the original simulator, but with a much faster speed that supports real-time decision makings.
主讲人简介
Prof. Jeff Hong received his bachelor’s and Ph.D. degrees from Tsinghua University and Northwestern University, respectively. He is currently with Fudan University, holding the positions of Fudan Distinguished Professor, Hongyi Chair Professor, Chair of Department of Management Science in School of Management, and Associate Dean of School of Data Science. He was Chair Professor of Management Sciences at City University of Hong Kong, and Professor of Industrial Engineering and Logistics Management at the Hong Kong University of Science and Technology. Prof. Hong’s research interests include stochastic simulation, stochastic optimization, risk management and supply chain management. He is currently the Simulation Area Editor of Operations Research, an Associate Editor of Management Science and ACM Transactions on Modeling and Computer Simulation, and he was the President of INFORMS Simulation Society from 2020 to 2022.
