您当前的位置:首页 > 科学研究 > 学术报告

科学研究

学术报告

Accurate and efficient numerical methods for molecular dynamics and data science using adaptive thermostats
发布时间:2018-04-08     点击次数:
报告题目: Accurate and efficient numerical methods for molecular dynamics and data science using adaptive thermostats
报 告 人: 商晓成 博士(瑞士苏黎世联邦理工)
报告时间: 2018年04月21日 16:00-16:40
报告地点: 理学院东北楼四楼报告厅(404)
报告摘要:

 I will discuss the design of state-of-the-art numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochastic dynamical methods, known as adaptive thermostats that automatically correct thermodynamic averages using a negative feedback loop, are discussed which have application to molecular dynamics and Bayesian sampling techniques arising in emerging machine learning applications. I will also discuss the characteristics of different algorithms, including the convergence of averages and the accuracy of numerical discretizations.

打印】【关闭
设为首页 | 加入收藏 | 联系我们
电子邮箱:maths@whu.edu.cn  邮政编码:430072
地址:中国·武汉·武昌·珞珈山 武汉大学数学与统计学院