Deep learning as a tool for inverse problems resolution: a case study
Authors:
Open Access
- 27 July 2022
- journal article
- research article
- Published by Emerald Publishing in COMPEL - The international journal for computation and mathematics in electrical and electronic engineering
- Vol. 41 (6) , 2120-2133
Abstract
Purpose: This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver. Design/methodology/approach: Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems either as fast solvers for the direct problem or as straightforward inverse problem solvers, with reference to the TEAM 25 benchmark problem for the sake of exemplification. Findings: Using DNNs as straightforward inverse problem solvers has relevant advantages in terms of promptness but requires a careful treatment of the underlying problem ill-posedness. Originality/value: This work is one of the first attempts to exploit DNNs for inverse problem resolution in low-frequency electromagnetism. Results on the TEAM 25 test problem show the potential effectiveness of the approach but also highlight the need for a careful choice of the training data set.Keywords
This publication has 11 references indexed in Scilit:
- Deep learning methods for solving linear inverse problems: Research directions and paradigmsSignal Processing, 2020
- On instabilities of deep learning in image reconstruction and the potential costs of AIProceedings of the National Academy of Sciences, 2020
- Deep Learning Techniques for Inverse Problems in ImagingIEEE Journal on Selected Areas in Information Theory, 2020
- Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural NetworksIEEE Signal Processing Magazine, 2020
- On Deep Learning for Inverse Problems2018
- Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical MethodsIEEE Signal Processing Magazine, 2018
- Convolutional Neural Networks for Inverse Problems in Imaging: A ReviewIEEE Signal Processing Magazine, 2017
- Deep Convolutional Neural Network for Inverse Problems in ImagingIEEE Transactions on Image Processing, 2017
- Genetic Algorithms2007
- Inverse Problems in Geophysics1999