| Ben Leimkuhler (University of Edinburgh) |
| Wed 19 Oct 2016, 16:00 - 17:00 |
| JCMB 5327 |
If you have a question about this talk, please contact: Kostas Zygalakis (kzygalak)
Molecular models and data analytics problems give rise to gargantuan systems of stochastic differential equations (SDEs) whose paths ergodically sample multimodal probability distributions. An important challenge for the numerical analyst (or the chemist, or the physicist, or the engineer, or the data scientist) is the design of efficient numerical methods to generate these paths. For SDEs, the numerical perspective is just maturing, with important new methods (and, even more important, new procedures for their construction and analysis) becoming available. One of the interesting ideas is to design stochastic schemes with close attention to the error in invariant measures. I will discuss different examples including efficient schemes for constrained stochastic dynamics improving accuracy and stability in bio-MD [1,2], and some very recent work on parallel sampling algorithms [3].
[1] B. Leimkuhler and C. Matthews, Rational construction of stochastic-numerical methods for molecular sampling, Applied Mathematics Research Express, 2013.
[2] B. Leimkuhler and C. Matthews, Efficient molecular dynamics using geodesic integration and solvent-solute splitting, Proceedings of the Royal Society A, 2016.
[3] B. Leimkuhler, C. Matthews and J. Weare, Ensemble preconditioning for Markov chain Monte Carlo simulation, Arxiv: https://arxiv.org/abs/1607.03954