TY  - BOOK
AU  - Ren,Qiang
AU  - Wang,Yinpeng
AU  - Li,Yongzhong
AU  - Qi,Shutong
ED  - SpringerLink (Online service)
TI  - Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning
SN  - 9789811662614
AV  - QC665.S3 
PY  - 2022///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Telecommunication
KW  - Mathematics—Data processing
KW  - Mathematical physics
KW  - Computer simulation
KW  - Machine learning
KW  - Artificial intelligence
KW  - Microwaves, RF Engineering and Optical Communications
KW  - Computational Science and Engineering
KW  - Computational Physics and Simulations
KW  - Machine Learning
KW  - Artificial Intelligence
KW  - Electromagnetic waves -- Scattering -- Data processing
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-981-16-6261-4
ER  -