Designing Optimal Spectral Filters for Inverse Problems. - Julianne Chung; Matthias Chung; Dianne P. O'Leary (SIAM J. Sci. Comput., 2011)

Optimal Filters from Calibration Data for Image Deconvolution with Data Acquisition Error. - Julianne Chung; Matthias Chung; Dianne P. O'Leary (J. Math. Imaging Vis., 2012)

Experimental Design for Biological Systems. - Matthias Chung; Eldad Haber (SIAM J. Control. Optim., 2012)

An Effective Method for Parameter Estimation with PDE Constraints with Multiple Right-Hand Sides. - Eldad Haber; Matthias Chung; Felix Herrmann (SIAM J. Optim., 2012)

Computing optimal low-rank matrix approximations for image processing. - Julianne Chung; Matthias Chung (ACSSC, 2013)

A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems. - Matthias Chung; Qi Long; Brent A. Johnson (Stat. Comput., 2013)

An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices. - Julianne Chung; Matthias Chung (CoRR, 2014)

Simultaneous Source for non-uniform data variance and missing data. - Eldad Haber; Matthias Chung (CoRR, 2014)

Population modelling by examples ii. - Robert J. Smith?; Bruce Y. Lee; Aristides Moustakas; Andreas Zeigler; Mélanie Prague; Romualdo Santos; Matthias Chung; Robin Gras; Valery Forbes; Sixten Borg; Tracy Comans; Yifei Ma; et al. (SummerSim, 2016)

Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems. - Julianne Chung; Matthias Chung; J. Tanner Slagel; Luis Tenorio (CoRR, 2017)

Optimal Regularized Inverse Matrices for Inverse Problems. - Julianne Chung; Matthias Chung (SIAM J. Matrix Anal. Appl., 2017)

Optimal Experimental Design for Inverse Problems with State Constraints. - Lars Ruthotto; Julianne Chung; Matthias Chung (SIAM J. Sci. Comput., 2018)

Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems. - Julianne Chung; Matthias Chung; J. Tanner Slagel (SSVM, 2019)

Sampled Limited Memory Methods for Massive Linear Inverse Problems. - Julianne Chung; Matthias Chung; J. Tanner Slagel; Luis Tenorio (CoRR, 2019)

Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes. - Matthias Chung; Mickaël Binois; Robert B. Gramacy; Johnathan M. Bardsley; David J. Moquin; Amanda P. Smith; Amber M. Smith (SIAM J. Sci. Comput., 2019)

Learning Regularization Parameters of Inverse Problems via Deep Neural Networks. - Babak Maboudi Afkham; Julianne Chung; Matthias Chung (CoRR, 2021)

Efficient learning methods for large-scale optimal inversion design. - Julianne Chung; Matthias Chung; Silvia Gazzola; Mirjeta Pasha (CoRR, 2021)

The Variable Projected Augmented Lagrangian Method. - Matthias Chung; Rosemary Renaut (CoRR, 2022)

slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks. - Elizabeth Newman; Julianne Chung; Matthias Chung; Lars Ruthotto (SIAM J. Sci. Comput., 2022)

Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks. - Babak Maboudi Afkham; Julianne Chung; Matthias Chung (CoRR, 2023)

Image reconstructions using sparse dictionary representations and implicit, non-negative mappings. - Elizabeth Newman; Jack Michael Solomon; Matthias Chung (CoRR, 2023)

Least-squares finite element method for ordinary differential equations. - Matthias Chung; Justin Krueger; Honghu Liu (J. Comput. Appl. Math., 2023)

Physics-informed neural networks for predicting liquid dairy manure temperature during storage. - Rana A. Genedy; Matthias Chung; Jactone A. Ogejo (Neural Comput. Appl., 2023)

Paired Autoencoders for Inverse Problems. - Matthias Chung; Emma Hart; Julianne Chung; Bas Peters; Eldad Haber (CoRR, 2024)

Sparse L1-Autoencoders for Scientific Data Compression. - Matthias Chung; Rick Archibald; Paul J. Atzberger; Jack Michael Solomon (CoRR, 2024)

A physics-aware data-driven surrogate approach for fast atmospheric radiative transfer inversion. - Cristina Sgattoni; Luca Sgheri; Matthias Chung (CoRR, 2024)

Paired Wasserstein Autoencoders for Conditional Sampling. - Moritz Piening; Matthias Chung (CoRR, 2024)

A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization. - Emma Hart; Julianne Chung; Matthias Chung (CoRR, 2025)

Fast ℓ1-Regularized EEG Source Localization Using Variable Projection. - Jack Michael Solomon; Rosemary Renaut; Matthias Chung (CoRR, 2025)

5D free-running, reconstruction, variable projection, ADMM, VPAL. - Yitong Yang; Muhammad Naeem; Marly van Assen; Jerome Yerly; Davide Piccini; Matthias Stuber; John Oshinski; Matthias Chung (CoRR, 2025)

Good Things Come in Pairs: Paired Autoencoders for Inverse Problems. - Matthias Chung; Bas Peters; Michael Solomon (CoRR, 2025)