Matthias Chung

Orcid: 0000-0001-7822-4539

According to our database1, Matthias Chung authored at least 26 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
A physics-aware data-driven surrogate approach for fast atmospheric radiative transfer inversion.
CoRR, 2024

Sparse L<sup>1</sup>-Autoencoders for Scientific Data Compression.
CoRR, 2024

Paired Autoencoders for Inverse Problems.
CoRR, 2024

2023
Physics-informed neural networks for predicting liquid dairy manure temperature during storage.
Neural Comput. Appl., June, 2023

Least-squares finite element method for ordinary differential equations.
J. Comput. Appl. Math., 2023

Image reconstructions using sparse dictionary representations and implicit, non-negative mappings.
CoRR, 2023

Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks.
CoRR, 2023

2022
slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks.
SIAM J. Sci. Comput., August, 2022

The Variable Projected Augmented Lagrangian Method.
CoRR, 2022

2021
Efficient learning methods for large-scale optimal inversion design.
CoRR, 2021

Learning Regularization Parameters of Inverse Problems via Deep Neural Networks.
CoRR, 2021

2019
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes.
SIAM J. Sci. Comput., 2019

Sampled Limited Memory Methods for Massive Linear Inverse Problems.
CoRR, 2019

Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2019

2018
Optimal Experimental Design for Inverse Problems with State Constraints.
SIAM J. Sci. Comput., 2018

2017
Optimal Regularized Inverse Matrices for Inverse Problems.
SIAM J. Matrix Anal. Appl., 2017

Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems.
CoRR, 2017

2016

2014
Simultaneous Source for non-uniform data variance and missing data.
CoRR, 2014

An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices.
CoRR, 2014

2013
A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems.
Stat. Comput., 2013

Computing optimal low-rank matrix approximations for image processing.
Proceedings of the 2013 Asilomar Conference on Signals, 2013

2012
An Effective Method for Parameter Estimation with PDE Constraints with Multiple Right-Hand Sides.
SIAM J. Optim., 2012

Experimental Design for Biological Systems.
SIAM J. Control. Optim., 2012

Optimal Filters from Calibration Data for Image Deconvolution with Data Acquisition Error.
J. Math. Imaging Vis., 2012

2011
Designing Optimal Spectral Filters for Inverse Problems.
SIAM J. Sci. Comput., 2011


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