Large deviations principles for symplectic discretizations of stochastic linear Schrödinger Equation

Abstract

In this paper, we consider the large deviations principles (LDPs) for the stochastic linear Schrödinger equation and its symplectic discretizations. These numerical discretizations are the spatial semi-discretization based on spectral Galerkin method, and the further full discretizations with symplectic schemes in temporal direction. First, by means of the abstract Gärtner--Ellis theorem, we prove that the observable $B_T=\frac{u(T)}{T}$, $T>0$ of the exact solution $u$ is exponentially tight and satisfies an LDP on $L^2(0,\pi ;\mathbb{C})$. Then, we present the LDPs for both $\{B_T^M{\}}_{T>0}$ of the spatial discretization $\{u^M{\}}_{M\in \mathbb{N}}$ and $\{B_N^M{\}}_{N\in \mathbb{N}}$ of the full discretization $\{u_N^M{\}}_{M,N\in \mathbb{N}}$, where $B_T^M=\frac{u^M(T)}{T}$ and $B_N^M=\frac{u_N^M}{N\tau }$ are the discrete approximations of $B_T$. Further, we show that both the semi-discretization $\{u^M{\}}_{M\in \mathbb{N}}$ and the full discretization $\{u_N^M{\}}_{M,N\in \mathbb{N}}$ based on temporal symplectic schemes can weakly asymptotically preserve the LDP of $\{B_T{\}}_{T>0}$. These results show the ability of symplectic discretizations to preserve the LDP of the stochastic linear \xde equation, and first provide an effective approach to approximating the LDP rate function in infinite dimensional space based on the numerical discretizations.

References in corpus (2)

• The large deviation approach to statistical mechanics
• Special invited paper. Large deviations

Cited by in corpus (4)

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• Asymptotics of large deviations of finite difference method for stochastic Cahn--Hilliard equation
• Large deviations principles of sample paths and invariant measures of numerical methods for parabolic SPDEs