报告题目：弱形式生成模型：抽样随机动力系统不变测度的Weak Generative Sampler

报 告 人：周翔副教授（香港城市大学）

报告时间：2025年4月2日  15:00

报告地点：格物楼数学研究中心528

报告摘要：

如何对随机动力系统中的随机变量进行高效采样，是随机计算领域的核心挑战之一。传统的随机微分方程数值方法在长时间模拟中面临效率问题，同时生成的样本通常存在时序相关性；而通过求解福克-普朗克方程得到概率密度函数的方法则无法直接用于样本采样。本次报告介绍了一种新型的数值方法，该方法通过深入挖掘福克-普朗克方程的弱形式以及概率密度流的生成模型算法，构造基于随机观测函数的损失函数，从而实现了高效生成满足复杂随机系统不变分布的独立同分布样本。该研究由蔡志强、曹语、黄远飞共同完成，相关成果详见预印本arxiv:2405.19256。

报告人简介：

Xiang ZHOU is the associate professor at Department of Mathematics and Department of Data Science in City University of Hong Kong. Xiang ZHOU received the BSc from Peking University (School of Mathematical Sciences) and PhD from Princeton University (PACM). Before joining the Department of Mathematics City University of Hong Kong in 2012, he worked as a research associate at Princeton University and Brown University. His research focuses on the numerical and mathematical aspects of computational algorithms for scientific computing problems in complex dynamical systems arising in natural science and engineering. His work integrates tools from probability theory, stochastic processes, dynamical systems, numerical analysis, optimization, optimal control, and machine learning to design efficient methods for better understanding complex phenomena such as rare events.