题目：分析新冠影响的随机动力学模型和统计物理模型
Stochastic dynamics and statistical physics models for the impactof COVID
时间：2023年03月09日周四10：30－11：30
地点：腾讯会议 ID：463 1304 6117
报告人：王子琦（美国加州大学伯克利分校，土木及环境工程系）
主持人：胡楠（土木工程系）陈太聪（土木工程系）
欢迎广大师生参加！
土木与交通学院
2023年03月05日
报告人简介：
王子琦博士目前是加州大学伯克利分校（University of California at Berkeley）结构风险与多灾害可靠性方向的助理教授，曾于2015年和2010年在西南交通大学分别获得土木工程博士学位和学士学位。在加入加州大学伯克利分校前，王博士曾在广州大学任教。他的主要研究兴趣是土木工程的结构可靠性、随机振动和不确定性量化，此外，他还致力于研究概率方法在更广泛科学领域中的应用。
Dr. Ziqi Wang is an Assistant Professor in Structural Risk and Reliability for Multi-Hazards at the University of California, Berkeley. Dr. Wang received his Ph.D. and B.S. in Civil Engineering from Southwest Jiaotong University (China) in 2015 and 2010. Prior to joining UC Berkeley as an Assistant Professor, he served as a faculty member at Guangzhou University (China). His main research interest lies in Structural Reliability, Random Vibration, and Uncertainty Quantification for Civil Engineering. He is also interested in applying probabilistic methods to a broader field of science.
报告摘要：
在这次报告中，王博士将介绍i）一个分析新冠病毒宏观影响的随机动力学模型和ii）一个分析新冠病毒对人群身体和心理交叉影响的微观统计物理模型。研究i）的科学目标是构造基于熵的指标来弥补基本繁殖数(R_0)的局限性，这一目标可通过开发具有时变参数的非线性马尔科夫过程模型来实现。研究ii）的科学目标属于更广泛的系统工程范畴，即分析异质和同质 系统力之间的竞争，从而使集体意见在应对风险时去极化/极化，这一目标可初步由嵌入表示身体和心理交叉影响层的多重复杂网络的类伊辛模型来实现。最后，王博士将分享他对复杂系统概率分析相关挑战的看法。
In this talk, I will present i) a macroscopic stochastic dynamics model designed to analyze the impact of COVID and ii) a microscopic statistical physics model designed to analyze the physical and psychological interactions among a group of individuals impacted by COVID. The scientific goal of research i) is to complement the well-known basic reproduction number (R_0) by entropy-based metrics. This goal is achieved by developing nonlinear Markov process models with parsimonious time-dependent parameters. The scientific objective of research ii) falls into a broader context of system engineering--analyzing the competition between heterogeneous and homogeneous “system forces” to depolarize/polarize collective opinions/responses toward a hazard. This goal is preliminarily achieved by developing Ising-like models framed into a multiplex network with layers representing physical and psychological interactions. Finally, I will share my views on the grand challenges of probabilistic analysis for complex systems.
