Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning [electronic resource] / by Qiang Ren, Yinpeng Wang, Yongzhong Li, Shutong Qi.
Material type: TextLanguage: İngilizce Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 online resourceContent type:- text
- computer
- online resource
- 9789811662614
- Telecommunication
- Mathematics—Data processing
- Mathematical physics
- Computer simulation
- Machine learning
- Artificial intelligence
- Microwaves, RF Engineering and Optical Communications
- Computational Science and Engineering
- Computational Physics and Simulations
- Machine Learning
- Artificial Intelligence
- Electromagnetic waves -- Scattering -- Data processing
- Machine learning
- QC665.S3
Item type | Current library | Home library | Collection | Call number | Copy number | Status | Notes | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|---|---|
E-Book | Merkez Kütüphane | Merkez Kütüphane | E-Kitap Koleksiyonu | QC665.S3EBK (Browse shelf(Opens below)) | 1 | Geçerli değil-e-Kitap / Not applicable-e-Book | ELE | EBK03098 |
Introduction to Electromagnetic Problems -- Basic Principles of Unveiling Electromagnetic Problems Based on Deep Learning -- Building Database -- Two-Dimensional Electromagnetic Scattering Solver -- Three-Dimensional Electromagnetic Scattering Solver.
This book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.
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