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Deterministic, stochastic, and deep learning methods for computational electromagnetics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deterministic, stochastic, and deep learning methods for computational electromagnetics/ by Wei Cai.
Author:	Cai, Wei.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxiv, 620 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Dielectric constant and fluctuation formulae for molecular dynamics -- Poisson-Boltzmann electrostatics and analytical approximations -- Numerical methods for Poisson-Boltzmann equations -- Random walk stochastic methods for boundary value problems -- Deep Neural Network for Solving PDEs -- Fast algorithms for long-range interactions -- Fast multipole methods for long-range interactions in layered media -- Maxwell equations, potentials, and physical/artificial boundary conditions -- Dyadic Green's functions in layered media -- High-order methods for surface electromagnetic integral equations -- High-order hierarchical N'ed'elec edge elements -- Time-domain methods - discontinuous Galerkin method and Yee scheme -- Scattering in periodic structures and surface plasmons -- Schr¨ odinger equations for waveguides and quantum dots -- Quantum electron transport in semiconductors -- Non-equilibrium Green's function (NEGF) methods for transport -- Numerical methods for Wigner quantum transport -- Hydrodynamic electron transport and finite difference methods -- Transport models in plasma media and numerical methods.
Contained By:	Springer Nature eBook
Subject:	Electromagnetism - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-981-96-0100-4
ISBN:	9789819601004

Deterministic, stochastic, and deep learning methods for computational electromagnetics
Cai, Wei.

Deterministic, stochastic, and deep learning methods for computational electromagnetics[electronic resource] /by Wei Cai. - Second edition. - Singapore :Springer Nature Singapore :2025. - xxiv, 620 p. :ill. (some col.), digital ;24 cm.

Dielectric constant and fluctuation formulae for molecular dynamics -- Poisson-Boltzmann electrostatics and analytical approximations -- Numerical methods for Poisson-Boltzmann equations -- Random walk stochastic methods for boundary value problems -- Deep Neural Network for Solving PDEs -- Fast algorithms for long-range interactions -- Fast multipole methods for long-range interactions in layered media -- Maxwell equations, potentials, and physical/artificial boundary conditions -- Dyadic Green's functions in layered media -- High-order methods for surface electromagnetic integral equations -- High-order hierarchical N'ed'elec edge elements -- Time-domain methods - discontinuous Galerkin method and Yee scheme -- Scattering in periodic structures and surface plasmons -- Schr¨ odinger equations for waveguides and quantum dots -- Quantum electron transport in semiconductors -- Non-equilibrium Green's function (NEGF) methods for transport -- Numerical methods for Wigner quantum transport -- Hydrodynamic electron transport and finite difference methods -- Transport models in plasma media and numerical methods.

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.

ISBN: 9789819601004

Standard No.: 10.1007/978-981-96-0100-4doiSubjects--Topical Terms:

579713
Electromagnetism
--Mathematical models.

LC Class. No.: QC760.4.M37

Dewey Class. No.: 537.0151

Deterministic, stochastic, and deep learning methods for computational electromagnetics
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245 1 0 $a Deterministic, stochastic, and deep learning methods for computational electromagnetics $h [electronic resource] / $c by Wei Cai.
250 $a Second edition.
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300 $a xxiv, 620 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Dielectric constant and fluctuation formulae for molecular dynamics -- Poisson-Boltzmann electrostatics and analytical approximations -- Numerical methods for Poisson-Boltzmann equations -- Random walk stochastic methods for boundary value problems -- Deep Neural Network for Solving PDEs -- Fast algorithms for long-range interactions -- Fast multipole methods for long-range interactions in layered media -- Maxwell equations, potentials, and physical/artificial boundary conditions -- Dyadic Green's functions in layered media -- High-order methods for surface electromagnetic integral equations -- High-order hierarchical N'ed'elec edge elements -- Time-domain methods - discontinuous Galerkin method and Yee scheme -- Scattering in periodic structures and surface plasmons -- Schr¨ odinger equations for waveguides and quantum dots -- Quantum electron transport in semiconductors -- Non-equilibrium Green's function (NEGF) methods for transport -- Numerical methods for Wigner quantum transport -- Hydrodynamic electron transport and finite difference methods -- Transport models in plasma media and numerical methods.
520 $a 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.
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