Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics

Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics

Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics

by: Wei Cai (Author)

Publisher: Springer

Edition: Second Edition 2025

Publication Date: 2025-03-03

Language: English

Print Length: 644 pages

ISBN-10: 9819600995

ISBN-13: 9789819600991

Book Description

This book provides a well-balanced and comprehensive picture based on clear physics, solid mathematical formulation, and state-of-the-art useful numerical methods in deterministic, stochastic, deep neural network machine learning approaches for computer simulations of electromagnetic and transport processes in biology, microwave and optical wave devices, and nano-electronics. Computational research has become strongly influenced by interactions from many different areas including biology, physics, chemistry, engineering, etc. A multifaceted approach addressing the interconnection among mathematical algorithms and physical foundation and application is much needed to prepare graduate students and researchers in applied mathematics and sciences and engineering for innovative advanced computational research in many applications areas, such as biomolecular solvation in solvents, radar wave scattering, the interaction of lights with plasmonic materials, plasma physics, quantum dots, electronic structure, current flows in nano-electronics, and microchip designs, etc.

Editorial Reviews

This book provides a well-balanced and comprehensive picture based on clear physics, solid mathematical formulation, and state-of-the-art useful numerical methods in deterministic, stochastic, deep neural network machine learning approaches for computer simulations of electromagnetic and transport processes in biology, microwave and optical wave devices, and nano-electronics. Computational research has become strongly influenced by interactions from many different areas including biology, physics, chemistry, engineering, etc. A multifaceted approach addressing the interconnection among mathematical algorithms and physical foundation and application is much needed to prepare graduate students and researchers in applied mathematics and sciences and engineering for innovative advanced computational research in many applications areas, such as biomolecular solvation in solvents, radar wave scattering, the interaction of lights with plasmonic materials, plasma physics, quantum dots, electronic structure, current flows in nano-electronics, and microchip designs, etc.

Amazon Page

代发服务PDF电子书10立即求助
1111
打赏
未经允许不得转载:Wow! eBook » Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics

觉得文章有用就打赏一下文章作者

支付宝扫一扫

微信扫一扫