Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics
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 p...
-д хадгалсан:
| Үндсэн зохиолч: | |
|---|---|
| Байгууллагын зохиогч: | |
| Формат: | Цахим Цахим ном |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
|
| Хэвлэл: | 2nd ed. 2025. |
| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.1007/978-981-96-0100-4 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-981-96-0100-4 | ||
| 003 | DE-He213 | ||
| 005 | 20260810165512.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 250302s2025 si | o |||| 0|eng d | ||
| 020 | |a 9789819601004 |9 978-981-96-0100-4 | ||
| 024 | 7 | |a 10.1007/978-981-96-0100-4 |2 doi | |
| 050 | 4 | |a QA297-299.4 | |
| 072 | 7 | |a PBKS |2 bicssc | |
| 072 | 7 | |a MAT041000 |2 bisacsh | |
| 072 | 7 | |a PBKS |2 thema | |
| 082 | 0 | 4 | |a 518 |2 23 |
| 100 | 1 | |a Cai, Wei. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics |h [electronic resource] / |c by Wei Cai. |
| 250 | |a 2nd ed. 2025. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XXIV, 620 p. 68 illus.). |b 22 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 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. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Numerical analysis. | |
| 650 | 0 | |a Differential equations. | |
| 650 | 0 | |a Electrical engineering. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Mathematics |x Data processing. | |
| 650 | 0 | |a Mathematical physics. | |
| 650 | 1 | 4 | |a Numerical Analysis. |
| 650 | 2 | 4 | |a Differential Equations. |
| 650 | 2 | 4 | |a Electrical and Electronic Engineering. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computational Mathematics and Numerical Analysis. |
| 650 | 2 | 4 | |a Theoretical, Mathematical and Computational Physics. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9789819600991 |
| 776 | 0 | 8 | |i Printed edition: |z 9789819601011 |
| 776 | 0 | 8 | |i Printed edition: |z 9789819601028 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-981-96-0100-4 |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 912 | |a ZDB-2-251 | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||
| 950 | |a Focus Bundle Mathematics and Statistics (SpringerNature-85421) | ||