Mean-square stability analysis of approximations of stochastic differential equations in infinite dimensions
Artikel i vetenskaplig tidskrift, 2017
The (asymptotic) behaviour of the second moment of solutions to stochastic differential equations is treated in mean-square stability analysis. This property is discussed for approximations of infinite-dimensional stochastic differential equations and necessary and sufficient conditions ensuring mean-square stability are given. They are applied to typical discretization schemes such as combinations of spectral Galerkin, finite element, Euler–Maruyama, Milstein, Crank–Nicolson, and forward and backward Euler methods. Furthermore, results on the relation to stability properties of corresponding analytical solutions are provided. Simulations of the stochastic heat equation illustrate the theory.
finite element methods
rational approximations
spectral methods
Milstein scheme
Euler–Maruyama scheme
Asymptotic mean-square stability
Lévy processes
numerical approximations of stochastic differential equations
linear stochastic partial differential equations
Galerkin methods
Författare
Annika Lang
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Göteborgs universitet
Andreas Petersson
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Andreas Thalhammer
BIT Numerical Mathematics
0006-3835 (ISSN) 1572-9125 (eISSN)
Vol. 57 4 963-990Ämneskategorier
Beräkningsmatematik
Sannolikhetsteori och statistik
DOI
10.1007/s10543-017-0684-7