Accurate and efficient numerical methods for molecular dynamics and data science using adaptive thermostats

Dr Xiaocheng Shang 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. 

Dr Xiaocheng Shang will also discuss the characteristics of different algorithms, including the convergence of averages and the accuracy of numerical discretizations.

Venue: Fulton Building, Room G20