Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics
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Beschreibung
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.
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.
Focuses on various numerical methods/algorithms for simulating electromagnetic phenomena Presents state-of-the-art deterministic, stochastic, and neural network machine learning methods for solving PDEs Provides fast multipole methods for Helmholtz equations in 3D layered media, random walk method, DFT computation
Autor*in
Wei Cai
Themen in »Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics«
Fast multipole methods Feynman-kac formula based probabilistic methods for PDEs Deep neural network learning algorithms for PDEs Boundary integral methods Discontinuous Galerkin methods Nedelec finite element methods WENO finite difference method Quantum Wigner equations Non-equilibrim Green’s function methods Particle-in-cell method