---

＜E-Book＞
Numerical Approximations of Stochastic Maxwell Equations : via Structure-Preserving Algorithms / by Chuchu Chen, Jialin Hong, Lihai Ji
(Lecture Notes in Mathematics. ISSN:16179692 ; 2341)

Edition	1st ed. 2023.
Publisher	(Singapore : Springer Nature Singapore : Imprint: Springer)
Year	2023
Language	English
Size	XVII, 284 p. 1 illus : online resource
Authors	*Chen, Chuchu author Hong, Jialin author Ji, Lihai author SpringerLink (Online service)
Subjects	LCSH:Numerical analysis LCSH:Probabilities LCSH:Differential equations FREE:Numerical Analysis FREE:Probability Theory FREE:Differential Equations
Notes	Introduction -- Solution Theory of Stochastic Maxwell Equations -- Intrinsic Properties of Stochastic Maxwell Equations -- Structure-Preserving Algorithms for Stochastic Maxwell Equations -- Convergence Analysis of Structure-Preserving Algorithms -- Implementation of Numerical Experiments -- Appendix A: Basic Identities and Inequalities -- Appendix B: Semigroup, Sobolev Space, and Differential Calculus -- Appendix C: Estimates Related to Maxwell Operators -- Appendix D: Some Results of Stochastic Partial Differential Equations -- References The stochastic Maxwell equations play an essential role in many fields, including fluctuational electrodynamics, statistical radiophysics, integrated circuits, and stochastic inverse problems. This book provides some recent advances in the investigation of numerical approximations of the stochastic Maxwell equations via structure-preserving algorithms. It presents an accessible overview of the construction and analysis of structure-preserving algorithms with an emphasis on the preservation of geometric structures, physical properties, and asymptotic behaviors of the stochastic Maxwell equations. A friendly introduction to the simulation of the stochastic Maxwell equations with some structure-preserving algorithms is provided using MATLAB for the reader’s convenience. The objects considered in this book are related to several fascinating mathematical fields: numerical analysis, stochastic analysis, (multi-)symplectic geometry, large deviations principle, ergodic theory, partial differential equation, probability theory, etc. This book will appeal to researchers who are interested in these topics HTTP:URL=https://doi.org/10.1007/978-981-99-6686-8

 Similar Items