東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deterministic, stochastic, and deep ...

Cai, Wei.

FindBook

Google Book

Amazon

博客來

Deterministic, stochastic, and deep learning methods for computational electromagnetics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deterministic, stochastic, and deep learning methods for computational electromagnetics/ by Wei Cai.
作者:	Cai, Wei.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xxiv, 620 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Electromagnetism - Mathematical models. -
電子資源:	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
LDR:03260nmm a2200337 a 4500 001 2409445
003 DE-He213
005 20250302115237.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819601004 $q (electronic bk.)
020 $a 9789819600991 $q (paper)
024 7 $a 10.1007/978-981-96-0100-4 $2 doi
035 $a 978-981-96-0100-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QC760.4.M37
072 7 $a PBKS $2 bicssc
072 7 $a MAT041000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 537.0151 $2 23
090 $a QC760.4.M37 $b C133 2025
100 1 $a Cai, Wei. $3 811470
245 1 0 $a Deterministic, stochastic, and deep learning methods for computational electromagnetics $h [electronic resource] / $c by Wei Cai.
250 $a Second edition.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
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.
650 0 $a Electromagnetism $x Mathematical models. $3 579713
650 0 $a Electrostatics. $3 542300
650 0 $a Electron transport. $3 656243
650 1 4 $a Numerical Analysis. $3 892626
650 2 4 $a Differential Equations. $3 907890
650 2 4 $a Electrical and Electronic Engineering. $3 3591890
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 891040
650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 1066859
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-96-0100-4
950 $a Mathematics and Statistics (SpringerNature-11649)