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

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• Xiaocheng Shang (University of Birmingham, School of Mathematics)
• Wednesday 05 February 2020, 12:00-13:00
• Physics West 103.

If you have a question about this talk, please contact Sergey Sergeev.

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.

This talk is part of the Optimisation and Numerical Analysis Seminars series.

This talk is included in these lists:

• Optimisation and Numerical Analysis Seminars
• Physics West 103
• School of Mathematics events

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