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Deep learning as a tool for inverse problems resolution: a case study

Deep learning as a tool for inverse problems resolution: a case study This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.Design/methodology/approachDifferent 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.FindingsUsing 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/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

Deep learning as a tool for inverse problems resolution: a case study


Abstract

This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.Design/methodology/approachDifferent 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.FindingsUsing 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/valueThis 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.

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Publisher
Emerald Publishing
Copyright
© Sami Barmada, Alessandro Formisano, Dimitri Thomopulos and Mauro Tucci.
ISSN
0332-1649
eISSN
0332-1649
DOI
10.1108/compel-10-2021-0383
Publisher site
See Article on Publisher Site

Abstract

This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.Design/methodology/approachDifferent 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.FindingsUsing 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/valueThis 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.

Journal

COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic EngineeringEmerald Publishing

Published: Oct 3, 2022

Keywords: Deep learning; Topology optimization; Electromagnetic inverse problems; Optimization; Surrogate optimization

References