M
Marco A. Iglesias
Researcher at University of Nottingham
Publications - 46
Citations - 1550
Marco A. Iglesias is an academic researcher from University of Nottingham. The author has contributed to research in topics: Inverse problem & Kalman filter. The author has an hindex of 17, co-authored 44 publications receiving 1204 citations. Previous affiliations of Marco A. Iglesias include University of Texas at Austin & University of Warwick.
Papers
More filters
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Ensemble Kalman methods for inverse problems
TL;DR: In this paper, the ensemble Kalman filter (EnKF) was used to solve a wide class of inverse problems, including inversion of a compact linear operator, inversion and Eulerian velocity measurements at positive times to determine the initial condition in an incompressible fluid.
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The Ensemble Kalman Filter for Inverse Problems
TL;DR: It is demonstrated that the ensemble Kalman method for inverse problems provides a derivative-free optimization method with comparable accuracy to that achieved by traditional least-squares approaches, and that the accuracy is of the same order of magnitude as that achieve by the best approximation.
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Hierarchical Bayesian level set inversion
TL;DR: This paper demonstrates how the scale-sensitivity of the Bayesian approach can be circumvented by means of a hierarchical approach, using a single scalar parameter, leading to well-defined Gibbs-based MCMC methods found by alternating Metropolis–Hastings updates of the level set function and the hierarchical parameter.
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A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems
TL;DR: In this paper, a derivative-free computational framework for approximating solutions to nonlinear PDE-constrained inverse problems is proposed, where the derivative of the forward operator and its adjoint are replaced with empirical covariances from an ensemble of elements from the admissible space of solutions.
Journal ArticleDOI
A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems
TL;DR: In this paper, a derivative-free computational framework for approximating solutions to nonlinear PDE-constrained inverse problems is proposed, in which the derivative of the forward operator and its adjoint are replaced with empirical covariances from an ensemble of elements from the admissible space of solutions.