Title:Accurate and efficient numerical methods for molecular dynamics and data science using adaptive thermostats
Speaker: Xiaocheng Shang (University of Birmingham)
Abstract: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.
Time: 08/03（Saturday）, 15:00-16:00
Location: Mingde Building B201-1
