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Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports

Foundations of computational mathematics, 2022-12, Vol.22 (6), p.1863-1922

2022

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Certified dimension reduction in nonlinear Bayesian inverse problems

Mathematics of computation, 2022-07, Vol.91 (336), p.1789-1835

2022

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Semivariogram methods for modeling Whittle-Matérn priors in Bayesian inverse problems

Inverse problems, 2020-05, Vol.36 (5), p.55006

2020

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Identification of community structure-based brain states and transitions using functional MRI

NeuroImage (Orlando, Fla.), 2021-12, Vol.244, p.118635-118635, Article 118635

2021

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A non-linear reverse-engineering method for inferring genetic regulatory networks

PeerJ (San Francisco, CA), 2020-04, Vol.8, p.e9065-e9065, Article e9065

2020

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Design of a Rainfall Sensor for Mobile Offshore Platform

Journal of physics. Conference series, 2020-09, Vol.1634 (1), p.12137

2020

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Data-free likelihood-informed dimension reduction of Bayesian inverse problems

Inverse problems, 2021-04, Vol.37 (4), p.45009

2021

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Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction

Journal of computational physics, 2023-07, Vol.485, p.112103, Article 112103

2023

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Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Journal of computational physics, 2016-06, Vol.315 (C), p.363-387

2016

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Multifidelity importance sampling

Computer methods in applied mechanics and engineering, 2016-03, Vol.300 (C), p.490-509

2016

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Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events

SIAM journal on scientific computing, 2024-02, Vol.46 (1), p.C1-C29

2024

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Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems

Inverse problems, 2024-03, Vol.40 (3), p.35005

2024

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Dimension-independent likelihood-informed MCMC

Journal of computational physics, 2016-01, Vol.304, p.109-137

2016

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Data-driven model reduction for the Bayesian solution of inverse problems

International journal for numerical methods in engineering, 2015-05, Vol.102 (5), p.966-990

2015

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A posteriori stochastic correction of reduced models in delayed‐acceptance MCMC, with application to multiphase subsurface inverse problems

International journal for numerical methods in engineering, 2019-06, Vol.118 (10), p.578-605

2019

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Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems

Inverse problems, 2022-12, Vol.38 (12), p.124002

2022

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Optimal Low-rank Approximations of Bayesian Linear Inverse Problems

SIAM journal on scientific computing, 2015-01, Vol.37 (6), p.A2451-A2487

2015

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Scalable Optimization-Based Sampling on Function Space

SIAM journal on scientific computing, 2020-01, Vol.42 (2), p.A1317-A1347

2020

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Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems

SIAM/ASA journal on uncertainty quantification, 2021-01, Vol.9 (1), p.29-64

2021

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Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems

Journal of scientific computing, 2024-06, Vol.99 (3), p.77-77, Article 77

2024

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Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems

SIAM journal on scientific computing, 2017-01, Vol.39 (5), p.S167-S196

2017

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Randomized reduced forward models for efficient Metropolis–Hastings MCMC, with application to subsurface fluid flow and capacitance tomography

GEM international journal on geomathematics, 2020-12, Vol.11 (1), Article 26

2020

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Extraction of high-resolution structural orientations from digital data: A Bayesian approach

Journal of structural geology, 2019-05, Vol.122, p.106-115

2019

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Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Journal of computational physics, 2016-03, Vol.315 (C)

2016

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On dimension reduction in Gaussian filters

Inverse problems, 2016-04, Vol.32 (4), p.45003

2016

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Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports

Certified dimension reduction in nonlinear Bayesian inverse problems

Semivariogram methods for modeling Whittle-Matérn priors in Bayesian inverse problems

Identification of community structure-based brain states and transitions using functional MRI

A non-linear reverse-engineering method for inferring genetic regulatory networks

Design of a Rainfall Sensor for Mobile Offshore Platform

Data-free likelihood-informed dimension reduction of Bayesian inverse problems

Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Multifidelity importance sampling

Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events

Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems

Dimension-independent likelihood-informed MCMC

Data-driven model reduction for the Bayesian solution of inverse problems

A posteriori stochastic correction of reduced models in delayed‐acceptance MCMC, with application to multiphase subsurface inverse problems

Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems

Optimal Low-rank Approximations of Bayesian Linear Inverse Problems

Scalable Optimization-Based Sampling on Function Space

Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems

Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems

Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems

Randomized reduced forward models for efficient Metropolis–Hastings MCMC, with application to subsurface fluid flow and capacitance tomography

Extraction of high-resolution structural orientations from digital data: A Bayesian approach

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

On dimension reduction in Gaussian filters

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