Symplectic Integration of Stochastic Hamiltonian Systems: Lecture Notes in Mathematics, cartea 2314
Autor Jialin Hong, Liying Sunen Limba Engleză Paperback – 22 feb 2023
The problems considered in this book are related to several fascinating research hotspots: numerical analysis, stochastic analysis, ergodic theory, stochastic ordinary and partial differential equations, and rough path theory. This book will appeal to researchers who are interested in these topics.
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Specificații
ISBN-13: 9789811976698
ISBN-10: 9811976694
Pagini: 300
Ilustrații: XII, 300 p. 1 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.48 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Seria Lecture Notes in Mathematics
Locul publicării:Singapore, Singapore
ISBN-10: 9811976694
Pagini: 300
Ilustrații: XII, 300 p. 1 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.48 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Seria Lecture Notes in Mathematics
Locul publicării:Singapore, Singapore
Cuprins
Chapter 1 Deterministic Hamiltonian System.- Chapter 2 Stochastic Hamiltonian System.- Chapter 3 Stochastic Structure Preserving Numerical Integrators.- Chapter 4 Stochastic Modified Equation and Its Applications.- Chapter 5 Stochastic Hamiltonian Partial Differential Equation.
Notă biografică
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.
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.
Textul de pe ultima copertă
This book provides an accessible overview concerning the stochastic numerical methods inheriting long-time dynamical behaviours of finite and infinite-dimensional stochastic Hamiltonian systems. The long-time dynamical behaviours under study involve symplectic structure, invariants, ergodicity and invariant measure. The emphasis is placed on the systematic construction and the probabilistic superiority of stochastic symplectic methods, which preserve the geometric structure of the stochastic flow of stochastic Hamiltonian systems.
The problems considered in this book are related to several fascinating research hotspots: numerical analysis, stochastic analysis, ergodic theory, stochastic ordinary and partial differential equations, and rough path theory. This book will appeal to researchers who are interested in these topics.
The problems considered in this book are related to several fascinating research hotspots: numerical analysis, stochastic analysis, ergodic theory, stochastic ordinary and partial differential equations, and rough path theory. This book will appeal to researchers who are interested in these topics.
Caracteristici
Gives an introduction to symplectic structure and stochastic variational principle for stochastic Hamiltonian systems Provides symplectic and conformal symplectic methods and ergodic methods via stochastic generating function Presents the superiority of symplectic methods for stochastic Hamiltonian systems based on large deviation theory