This book provides some recent advance in the study of stochastic nonlinear Schrödinger equations and their numerical approximations, including the well-posedness, ergodicity, symplecticity and multi-symplecticity. It gives an accessible overview of the existence and uniqueness of invariant measures for stochastic differential equations, introduces geometric structures including symplecticity and (conformal) multi-symplecticity for nonlinear Schrödinger equations and their numerical approximations, and studies the properties and convergence errors of numerical methods for stochastic nonlinear Schrödinger equations.
This book will appeal to researchers who are interested in numerical analysis, stochastic analysis, ergodic theory, partial differential equation theory, etc.

Gives a detailed introduction to ergodicity and symplectic and multi-symplectic structures for stochastic nonlinear Schrödinger equations Provides the study of ergodic numerical approximations for stochastic nonlinear Schrödinger equations without strong dissipative terms Constructs numerical approximations which inherit both dynamical behaviors and geometric structures even for stochastic nonlinear Schrödinger equation of conservative type

Autorentext

Jialin Hong is a professor at the Chinese Academy of Sciences. He obtained his Ph.D. in 1994 at Jilin University. He works in various directions including structure-preserving algorithms for dynamical systems involving symplectic and multi-symplectic methods for Hamiltonian ODEs and PDEs, Lie group methods and applications, numerical dynamics including chaos, bifurcations for discrete systems, numerical methods for stochastic ordinary differential systems, stochastic partial differential equations and backward stochastic differential equations, almost periodic dynamical systems, and ergodic theory. Liying Sun is a postdoctoral researcher in the Chinese Academy of Sciences. She works in stochastic differential equations and their numerical methods. She has been investigating regularity properties and strong convergence of numerical approximations for stochastic partial differential equations, weak convergence and numerical longtime behaviors of numerical approximations for stochastic partial differential equations, structure-preserving numerical methods including symplectic integrators and energy-preserving integrators for stochastic Hamiltonian system.

Inhalt
Invariant measures and ergodicity.- Invariant measures for stochastic differential equations.- Invariant measures for stochastic nonlinear Schrödinger equations.- Geometric structures and numerical schemes for nonlinear Schrödinger equations.- Numerical invariant measures for damped stochastic nonlinear Schrödinger equations.- Approximation of ergodic limit for conservative stochastic nonlinear Schrödinger equations.