Applications of deep learning in electromagnetics: teaching Maxwell's equations to machines
Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Stevenage
The Institution of Engineering and Technology
2022
|
| Schriftenreihe: | The ACES series on computational and numerical modelling in electrical engineering
Electromagnetic waves |
| Online-Zugang: | TUM01 UBY01 URL des Erstveröffentlichers |
| Zusammenfassung: | Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate. With the development of deep learning techniques, improvement in learning capacity and generalization ability may allow machines to "learn" from properly collected data and "master" the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with knowledge from training data could unleash numerous possibilities in electromagnetic theory and engineering that used to be impossible due to the limit of data information and ability of computation. Electromagnetic applications of deep learning covered in the book include electromagnetic forward modeling, free-space inverse scattering, non-destructive testing and evaluation, subsurface imaging, biomedical imaging, direction of arrival estimation, remote sensing, digital satellite communications, imaging and gesture recognition, metamaterials and metasurfaces design, as well as microwave circuit modeling. <italic>Applications of Deep Learning in Electromagnetics</italic> contains valuable information for researchers looking for new tools to solve Maxwell's equations, students of electromagnetic theory, and researchers in the field of deep learning with an interest in novel applications. |
| Beschreibung: | 1 Online-Ressource (xvii, 458 Seiten) |
| ISBN: | 9781839535901 |
| DOI: | 10.1049/SBEW563E |
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| id | DE-604.BV048933566 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:57:47Z |
| indexdate | 2024-07-10T09:50:18Z |
| institution | BVB |
| isbn | 9781839535901 |
| language | English |
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| physical | 1 Online-Ressource (xvii, 458 Seiten) |
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| publisher | The Institution of Engineering and Technology |
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| series2 | The ACES series on computational and numerical modelling in electrical engineering Electromagnetic waves |
| spelling | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines edited by Maokun Li and Marco Salucci Stevenage The Institution of Engineering and Technology 2022 1 Online-Ressource (xvii, 458 Seiten) txt rdacontent c rdamedia cr rdacarrier The ACES series on computational and numerical modelling in electrical engineering Electromagnetic waves Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate. With the development of deep learning techniques, improvement in learning capacity and generalization ability may allow machines to "learn" from properly collected data and "master" the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with knowledge from training data could unleash numerous possibilities in electromagnetic theory and engineering that used to be impossible due to the limit of data information and ability of computation. Electromagnetic applications of deep learning covered in the book include electromagnetic forward modeling, free-space inverse scattering, non-destructive testing and evaluation, subsurface imaging, biomedical imaging, direction of arrival estimation, remote sensing, digital satellite communications, imaging and gesture recognition, metamaterials and metasurfaces design, as well as microwave circuit modeling. <italic>Applications of Deep Learning in Electromagnetics</italic> contains valuable information for researchers looking for new tools to solve Maxwell's equations, students of electromagnetic theory, and researchers in the field of deep learning with an interest in novel applications. Li, Maokun edt Salucci, Marco edt Erscheint auch als Druck-Ausgabe 9781839535895 https://doi.org/10.1049/SBEW563E Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines |
| title | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines |
| title_auth | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines |
| title_exact_search | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines |
| title_exact_search_txtP | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines |
| title_full | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines edited by Maokun Li and Marco Salucci |
| title_fullStr | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines edited by Maokun Li and Marco Salucci |
| title_full_unstemmed | Applications of deep learning in electromagnetics teaching Maxwell's equations to machines edited by Maokun Li and Marco Salucci |
| title_short | Applications of deep learning in electromagnetics |
| title_sort | applications of deep learning in electromagnetics teaching maxwell s equations to machines |
| title_sub | teaching Maxwell's equations to machines |
| url | https://doi.org/10.1049/SBEW563E |
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