Light PhD Seminar: Hybrid Monte Carlo methods for sampling in complex physical systems

Efficient sampling is the key to success of molecular simulation of complex physical systems. Still, a unique recipe for achieving this goal is unavailable. Hybrid Monte Carlo (HMC) is a promising sampling tool offering a smart, free of discretization errors, propagation in phase space, rigorous temperature control and flexibility. However, its inability to provide dynamical information and its weakness in simulations of reasonably large systems do not allow HMC to become a sampler of choice in molecular simulation of complex systems.

In this talk, an algorithmic introduction to the HMC method will be provided. In addition, it will be shown that the performance of HMC can be dramatically improved by introducing in the method techniques such as splitting numerical integrators and importance sampling. We show that equipping the Hybrid Monte Carlo algorithm with extra features makes it even a "smarter" sampler and a strong competitor to the well-established molecular simulation techniques such as molecular dynamics and Monte Carlo.