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...
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | inglés |
| Publicado: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
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| Edición: | 2nd ed. 2025. |
| Subjects: | |
| Acceso en liña: | https://doi.org/10.1007/978-981-96-0100-4 |
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Table of Contents:
- 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.