Uncertainty Quantification for Inverse Problems
Uncertainty Quantification (UQ) is an interesting, fast growing research area aimed at developing methods to address the impact of parameter, data and model uncertainty in complex systems. In this talk we will focus on the identification of parameters through observations of the response of the system – the inverse problem. The uncertainty in the solution of the inverse problem will be described via the Bayesian approach. In cases, where the model evaluations are prohibitively expensive, ad hoc methods such as the Ensemble Kalman Filter (EnKF) for inverse problems are widely and successfully used by practitioners in order to approximate the solution of the Bayesian problem.
The low computational costs, the straightforward implementation and their non-intrusive nature make them appealing in various areas of application, but, on the downside, they are underpinned by very limited theoretical understanding. In this talk, we will discuss an analysis of the EnKF based on the continuous time scaling limits, whichallows to derive estimates on the long-time behaviour of the EnKF and, hence, provides insights into the convergence properties of the algorithm. In particular, we are interested in the properties of the EnKF for a fixed ensemble size. Results from various numerical experiments supporting the theoretical findings will be presented.