41 citations · 198 across the 24 of their papers we have counts for
9 papers · 1 filter
Self-reinforced polynomial approximation methods for concentrated probability densities
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Transport map methods offer a powerful statistical learning tool that can couple a target high-dimensional random variable with some reference random variable using invertible tran…
Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems
Tiangang Cui, Xin Tong, Olivier Zahm
Markov chain Monte Carlo (MCMC) methods form one of the algorithmic foundations of Bayesian inverse problems. The recent development of likelihood-informed subspace (LIS) methods o…
Nonlinear dimension reduction for surrogate modeling using gradient information
Daniele Bigoni, Youssef Marzouk, Clémentine Prieur +1
We introduce a method for the nonlinear dimension reduction of a high-dimensional function , . Our objective is to identify a nonlinear…
Randomized residual-based error estimators for the Proper Generalized Decomposition approximation of parametrized problems
Kathrin Smetana, Olivier Zahm
This paper introduces a novel error estimator for the Proper Generalized Decomposition (PGD) approximation of parametrized equations. The estimator is intrinsically random: It buil…
Multifidelity Dimension Reduction via Active Subspaces
Rémi Lam, Olivier Zahm, Youssef Marzouk +1
We propose a multifidelity dimension reduction method to identify a low-dimensional structure present in many engineering models. The structure of interest arises when functions va…
Randomized residual-based error estimators for parametrized equations
Kathrin Smetana, Olivier Zahm, Anthony T Patera
We propose a randomized a posteriori error estimator for reduced order approximations of parametrized (partial) differential equations. The error estimator has several important pr…
Projection based model order reduction methods for the estimation of vector-valued variables of interest
Olivier Zahm, Marie Billaud-Friess, Anthony Nouy
We propose and compare goal-oriented projection based model order reduction methods for the estimation of vector-valued functionals of the solution of parameter-dependent equations…
Interpolation of inverse operators for preconditioning parameter-dependent equations
Olivier Zahm, Anthony Nouy
We propose a method for the construction of preconditioners of parameter-dependent matrices for the solution of large systems of parameter-dependent equations. The proposed method…
A tensor approximation method based on ideal minimal residual formulations for the solution of high-dimensional problems
Marie Billaud-Friess, Anthony Nouy, Olivier Zahm
In this paper, we propose a method for the approximation of the solution of high-dimensional weakly coercive problems formulated in tensor spaces using low-rank approximation forma…