Data Assimilation Meetings at Reading
Date | Meeting type | Speakers |
12 February 2014 | Invited speaker |
Nikolas Kantas (Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A case study for the Navier-Stokes equations)
We consider the inverse problem of estimating the initial condition of a partial differential equation, which is only observed through noisy measurements at discrete time intervals. In particular, we focus on the case where Eulerian measurements are obtained from the time and space evolving vector field, whose evolution obeys the two-dimensional Navier-Stokes equations defined on a torus. We will adopt a Bayesian formulation resulting from a particular regularization that ensures the problem is well posed. In the context of Monte Carlo based inference, it is a challenging task to obtain samples from the resulting high dimensional posterior on the initial condition. Often, in data assimilation applications it is common for computational methods to invoke the use of heuristics and Gaussian approximations. In the presence of non-linear dynamics and observations, the resulting inferences are biased and not well-justified from a theoretical perspective. On the other hand, Monte Carlo methods can be used to assimilate data in a principled manner, but are often perceived as inefficient in this context due to the high-dimensionality of the problem. In this work we will propose a generic Sequential Monte Carlo (SMC) sampling approach for high dimensional inverse problems that overcomes some of these difficulties. The method builds upon appropriate Markov chain Monte Carlo (MCMC) techniques, which are currently considered as benchmarks for evaluating data assimilation algorithms used in practice. In our numerical examples, the proposed SMC approach achieves the same accuracy as MCMC but in a much more efficient manner. The talk is based on joint work with Alexandros Beskos (UCL & NUS), Ajay Jasra (NUS), Alexandre Thiery (NUS) and Dan Crisan (Imperial). |