O
Olivier Zahm
Researcher at French Institute for Research in Computer Science and Automation
Publications - 37
Citations - 467
Olivier Zahm is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Residual & Subspace topology. The author has an hindex of 10, co-authored 33 publications receiving 350 citations. Previous affiliations of Olivier Zahm include École centrale de Nantes & University of Grenoble.
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Certified dimension reduction in nonlinear Bayesian inverse problems
TL;DR: A dimension reduction technique for Bayesian inverse problems with nonlinear forward operators, non-Gaussian priors, and non- Gaussian observation noise is proposed and an analysis that enables control of the posterior approximation error due to this sampling is provided.
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Gradient-based dimension reduction of multivariate vector-valued functions
TL;DR: In this article, the authors propose a gradients for multivariate functions encountered in high-dimensional uncertainty quantification problems often vary most strongly along a few dominant directions in the input parameter space.
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Shared low-dimensional subspaces for propagating kinetic uncertainty to multiple outputs
Weiqi Ji,J. Wang,Olivier Zahm,Youssef M. Marzouk,Bin Yang,Zhuyin Ren,Chung K. Law,Chung K. Law +7 more
TL;DR: A new method is introduced that can simultaneously approximate the marginal probability density functions of multiple outputs using a single low-dimensional shared subspace that can accurately reproduce the probability of ignition failure and the probability density of ignition crank angle conditioned on successful ignition.
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A tensor approximation method based on ideal minimal residual formulations for the solution of high-dimensional problems
TL;DR: In this article, a perturbation of a minimal residual method with residual norm corresponding to the error in a specified solution norm is proposed for weakly coercive problems in tensor spaces.
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A tensor approximation method based on ideal minimal residual formulations for the solution of high-dimensional problems ∗
TL;DR: A weak greedy algorithm is introduced which uses this perturbed minimal residual method for the computation of successive greedy correc- tions in small tensor subsets and its convergence under some conditions on the parameters of the algorithm is proved.