8 citations · 8 across the 5 of their papers we have counts for
6 papers · 1 filter
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient goal-oriented uncertainty quantification for inverse problems. Contrary…
Efficient learning methods for large-scale optimal inversion design
Julianne Chung, Matthias Chung, Silvia Gazzola +1
In this work, we investigate various approaches that use learning from training data to solve inverse problems, following a bi-level learning approach. We consider a general framew…
Least-Squares Finite Element Method for Ordinary Differential Equations
Matthias Chung, Justin Krueger, Honghu Liu
We consider the least-squares finite element method (lsfem) for systems of nonlinear ordinary differential equations and establish an optimal error estimate for this method when pi…
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning ap…
Sampled Limited Memory Methods for Massive Linear Inverse Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to ne…
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant c…