A regularising ensemble Kalman method for PDE-constrained inverse problems By Prof. Marco Iglesias (University of Nottingham, U.K.)
We present a novel regularizing ensemble Kalman method for solving PDE-constrained inverse problems. By merging ideas from iterative regularisation approaches and ensemble Kalman algorithms we design a derivative-free solver for generic inverse problems. The proposed method can be used to estimate parameters of large-scale PDE models in a black-box fashion.
Overview
Abstract
We present a novel regularizing ensemble Kalman method for solving PDE-constrained inverse problems. By merging ideas from iterative regularisation approaches and ensemble Kalman algorithms we design a derivative-free solver for generic inverse problems. The proposed method can be used to estimate parameters of large-scale PDE models in a black-box fashion. We provide numerical results to illustrate the efficacy of the proposed method for solving inverse problems that arise in a wide range of applications such as subsurface flow, medical imaging and manufacturing composite materials.
Brief Biography
Marco Iglesias received his PhD in 2008 from the Institute for Computational Engineering and Sciences, University of Texas at Austin.
From 2008 and 2013 Iglesias held postdoctoral positions at the Department of Civil and Environmental Engineering at MIT as well as the Mathematics Institute at the University of Warwick. Since October 2013 Iglesias is an assistant professor in scientific computation at the University of Nottingham, U.K. His main research interest is the solution of PDE-constrained inverse problems. In particular, Iglesias' area of expertise lies in the computational aspects of classical and Bayesian inverse problems that arises from large-scale applications.
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