- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
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.
Applications of Deep Learning in Electromagnetics 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.
Contents
Chapter 1: An introduction to deep learning for electromagnetics
Chapter 2: Deep learning techniques for electromagnetic forward modeling
Chapter 3: Deep learning techniques for free-space inverse scattering
Chapter 4: Deep learning techniques for non-destructive testing and evaluation
Chapter 5: Deep learning techniques for subsurface imaging
Chapter 6: Deep learning techniques for biomedical imaging
Chapter 7: Deep learning techniques for direction of arrival estimation
Chapter 8: Deep learning techniques for remote sensing
Chapter 9: Deep learning techniques for digital satellite communications
Chapter 10: Deep learning techniques for imaging and gesture recognition
Chapter 11: Deep learning techniques for metamaterials and metasurfaces design
Chapter 12: Deep learning techniques for microwave circuit modeling
Chapter 13: Concluding remarks, open challenges, and future trends
