Youssef M. Marzouk
Massachusetts Institute of Technology
231 Papers
1.3K Citations
Youssef M. Marzouk is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Bayesian inference & Markov chain Monte Carlo. The author has an hindex of 38, co-authored 208 publications. Previous affiliations of Youssef M. Marzouk include Sandia National Laboratories & Université catholique de Louvain.
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Papers
Stochastic spectral methods for efficient Bayesian solution of inverse problems
TL;DR: This work presents a reformulation of the Bayesian approach to inverse problems, that seeks to accelerate Bayesian inference by using polynomial chaos expansions to represent random variables, and evaluates the utility of this technique on a transient diffusion problem arising in contaminant source inversion.
550
Simulation-based optimal Bayesian experimental design for nonlinear systems
Xun Huan,Youssef M. Marzouk +1 more
TL;DR: This work proposes a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models, and focuses on finding sets of experiments that provide the most information about targeted sets of parameters.
495
Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems
Youssef M. Marzouk,Habib N. Najm +1 more
TL;DR: This work considers a Bayesian approach to nonlinear inverse problems in which the unknown quantity is a spatial or temporal field, endowed with a hierarchical Gaussian process prior, and introduces truncated Karhunen-Loeve expansions, based on the prior distribution, to efficiently parameterize the unknown field.
456
Bayesian inference with optimal maps
Tarek Moselhy,Youssef M. Marzouk +1 more
TL;DR: A new approach to Bayesian inference is presented that entirely avoids Markov chain simulation, by constructing a map that pushes forward the prior measure to the posterior measure, and demonstrates the accuracy and efficiency of the approach on nonlinear inverse problems of varying dimension.
384
A stochastic collocation approach to Bayesian inference in inverse problems
Youssef M. Marzouk,Dongbin Xiu +1 more
TL;DR: In this paper, a generalized polynomial chaos (gPC) based forward solver for inverse problems is proposed. But the convergence rate of the forward solution is not known.