Artigos com autorizações de acesso público - Julianne ChungSaiba mais
21 artigos disponíveis publicamente
Learning regularization parameters of inverse problems via deep neural networks
BM Afkham, J Chung, M Chung
Inverse Problems 37 (10), 105017, 2021
Autorizações: US National Science Foundation, Villum Foundation
Computational methods for large-scale inverse problems: a survey on hybrid projection methods
J Chung, S Gazzola
Siam Review 66 (2), 205-284, 2024
Autorizações: UK Engineering and Physical Sciences Research Council
Flexible Krylov Methods for Regularization
J Chung, S Gazzola
SIAM Journal on Scientific Computing 41 (5), S149-S171, 2019
Autorizações: US National Science Foundation, UK Engineering and Physical Sciences …
Generalized hybrid iterative methods for large-scale Bayesian inverse problems
J Chung, AK Saibaba
SIAM Journal on Scientific Computing 39 (5), S24-S46, 2017
Autorizações: US National Science Foundation
Efficient generalized Golub–Kahan based methods for dynamic inverse problems
J Chung, AK Saibaba, M Brown, E Westman
Inverse Problems 34 (2), 024005, 2018
Autorizações: US National Science Foundation
Motion estimation and correction in photoacoustic tomographic reconstruction
J Chung, L Nguyen
SIAM Journal on Imaging Sciences 10 (1), 216-242, 2017
Autorizações: US National Science Foundation
Sampled Tikhonov regularization for large linear inverse problems
JT Slagel, J Chung, M Chung, D Kozak, L Tenorio
Inverse Problems 35 (11), 114008, 2019
Autorizações: US National Science Foundation
Optimal experimental design for inverse problems with state constraints
L Ruthotto, J Chung, M Chung
SIAM Journal on Scientific Computing 40 (4), B1080-B1100, 2018
Autorizações: US National Science Foundation
Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems
AK Saibaba, J Chung, K Petroske
Numerical Linear Algebra with Applications 27 (5), e2325, 2020
Autorizações: US National Science Foundation
High-performance three-dimensional image reconstruction for molecular structure determination
J Chung, P Sternberg, C Yang
The International Journal of High Performance Computing Applications 24 (2 …, 2010
Autorizações: US National Institutes of Health
Hybrid projection methods with recycling for inverse problems
J Jiang, J Chung, E De Sturler
SIAM Journal on Scientific Computing 43 (5), S146-S172, 2021
Autorizações: US National Science Foundation
Computationally efficient methods for large-scale atmospheric inverse modeling
T Cho, J Chung, SM Miller, AK Saibaba
Geoscientific Model Development 15 (14), 5547-5565, 2022
Autorizações: US National Science Foundation, US National Aeronautics and Space …
slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks
E Newman, J Chung, M Chung, L Ruthotto
SIAM Journal on Scientific Computing 44 (4), A2322-A2348, 2022
Autorizações: US National Science Foundation, US Department of Energy, US Department of …
Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors
T Cho, J Chung, J Jiang
Inverse Problems 37 (4), 044002, 2021
Autorizações: US National Science Foundation
Iterative sampled methods for massive and separable nonlinear inverse problems
J Chung, M Chung, JT Slagel
Scale Space and Variational Methods in Computer Vision: 7th International …, 2019
Autorizações: US National Science Foundation
Sampled limited memory methods for massive linear inverse problems
J Chung, M Chung, JT Slagel, L Tenorio
Inverse Problems 36 (5), 054001, 2020
Autorizações: US National Science Foundation
Recovering signals in physiological systems with large datasets
H Pendar, JJ Socha, J Chung
Biology open 5 (8), 1163-1174, 2016
Autorizações: US National Science Foundation
Computational tools for inversion and uncertainty estimation in respirometry
T Cho, H Pendar, J Chung
Plos one 16 (5), e0251926, 2021
Autorizações: US National Science Foundation
Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks
BM Afkham, J Chung, M Chung
Inverse Problems 40 (7), 075010, 2024
Autorizações: Villum Foundation
Quantifying uncertainties in large-scale Bayesian linear inverse problems using Krylov subspace methods
AK Saibaba, J Chung, K Petroske
arXiv preprint arXiv:1808.09066, 2018
Autorizações: US National Science Foundation
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