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06/14/2023
Flexible Krylov Methods for Group Sparsity Regularization
This paper introduces new solvers for efficiently computing solutions to...
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04/17/2023
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
In this work, we describe a new approach that uses variational encoder-d...
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06/14/2022
Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems
We develop hybrid projection methods for computing solutions to large-sc...
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10/06/2021
Efficient learning methods for large-scale optimal inversion design
In this work, we investigate various approaches that use learning from t...
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09/28/2021
slimTrain – A Stochastic Approximation Method for Training Separable Deep Neural Networks
Deep neural networks (DNNs) have shown their success as high-dimensional...
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05/15/2021
Computational methods for large-scale inverse problems: a survey on hybrid projection methods
This paper surveys an important class of methods that combine iterative ...
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04/14/2021
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
In this work, we describe a new approach that uses deep neural networks ...
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07/01/2020
Hybrid Projection Methods with Recycling for Inverse Problems
Iterative hybrid projection methods have proven to be very effective for...
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03/30/2020
Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors
When solving ill-posed inverse problems, a good choice of the prior is c...
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12/17/2019
Sampled Limited Memory Methods for Massive Linear Inverse Problems
In many modern imaging applications the desire to reconstruct high resol...
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02/23/2017