Bayesian Inference and Deep Learning for Inverse Problems-Reference-Cited by-同舟云学术

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Bayesian Inference and Deep Learning for Inverse Problems

Published:2023-12-01 Issue: Volume: Page:
ISSN:
Container-title:The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
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Author:

Mohammad-Djafari Ali¹²^ORCID,Chu Ning²,Wang Li³,Yu Liang⁴⁵^ORCID

Affiliation:

1. International Science Consulting and Training (ISCT), 91440 Bures sur Yvette, France

2. Zhejiang Shangfeng Special Blower Company, Shaoxing 312352, China

3. School of Mathematics and Statistics, Central South University, Changsha 410017, China

4. School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China

5. State Key Laboratory of Airliner Integration Technology and Flight Simulation, Shanghai 200126, China

Publisher

MDPI

Link

https://www.mdpi.com/2673-9984/9/1/14/pdf

Reference18 articles.

1. Joint NDT Image Restoration and Segmentation Using Gauss-Markov-Potts Prior Models and Variational Bayesian Computation;Ayasso;IEEE Trans. Image Process.,2010

2. The Practicality of Stochastic Optimization in Imaging Inverse Problems;Tang;IEEE Trans. Comput. Imaging,2020

3. Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification;Zhu;J. Comput. Phys.,2018

4. A Review of Convolutional Neural Networks for Inverse Problems in Imaging;McCann;Image Video Process.,2017

5. A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network;Fang;IEEE Trans. Neural Netw. Learn. Syst.,2021

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