Matthias Chung

According to our database, Matthias Chung authored at least 23 papers between 2011 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Physics-informed neural networks for predicting liquid dairy manure temperature during storage.

Rana A. Genedy, Matthias Chung, Jactone A. Ogejo

Neural Comput. Appl., June, 2023

Least-squares finite element method for ordinary differential equations.

Matthias Chung, Justin Krueger, Honghu Liu

J. Comput. Appl. Math., 2023

Image reconstructions using sparse dictionary representations and implicit, non-negative mappings.

Elizabeth Newman, Jack Michael Solomon, Matthias Chung

CoRR, 2023

Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks.

Babak Maboudi Afkham, Julianne Chung, Matthias Chung

CoRR, 2023

2022

slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks.

Elizabeth Newman, Julianne Chung, Matthias Chung, Lars Ruthotto

SIAM J. Sci. Comput., August, 2022

The Variable Projected Augmented Lagrangian Method.

Matthias Chung, Rosemary Renaut

CoRR, 2022

2021

Efficient learning methods for large-scale optimal inversion design.

Julianne Chung, Matthias Chung, Silvia Gazzola, Mirjeta Pasha

CoRR, 2021

Learning Regularization Parameters of Inverse Problems via Deep Neural Networks.

Babak Maboudi Afkham, Julianne Chung, Matthias Chung

CoRR, 2021

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

Sampled Limited Memory Methods for Massive Linear Inverse Problems.

Julianne Chung, Matthias Chung, J. Tanner Slagel, Luis Tenorio

CoRR, 2019

Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems.

Julianne Chung, Matthias Chung, J. Tanner Slagel

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2019

2018

Optimal Experimental Design for Inverse Problems with State Constraints.

Lars Ruthotto, Julianne Chung, Matthias Chung

SIAM J. Sci. Comput., 2018

2017

Optimal Regularized Inverse Matrices for Inverse Problems.

Julianne Chung, Matthias Chung

SIAM J. Matrix Anal. Appl., 2017

Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems.

Julianne Chung, Matthias Chung, J. Tanner Slagel, Luis Tenorio

CoRR, 2017

2016

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, Nieko Punt, William Jusko, Lucas Brotz, Ayaz Hyder

Proceedings of the Summer Computer Simulation Conference, 2016

2014

Simultaneous Source for non-uniform data variance and missing data.

Eldad Haber, Matthias Chung

CoRR, 2014

An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices.

Julianne Chung, Matthias Chung

CoRR, 2014

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

Computing optimal low-rank matrix approximations for image processing.

Julianne Chung, Matthias Chung

Proceedings of the 2013 Asilomar Conference on Signals, 2013

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

Experimental Design for Biological Systems.

Matthias Chung, Eldad Haber

SIAM J. Control. Optim., 2012

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

2011

Designing Optimal Spectral Filters for Inverse Problems.

Julianne Chung, Matthias Chung, Dianne P. O'Leary

SIAM J. Sci. Comput., 2011