Magnus-type Integrator for the Finite Element Discretization of Semilinear Parabolic non-Autonomous SPDEs Driven by multiplicative noise
Western Norway University of Applied Sciences(西挪威应用科学大学);Center for Research in Computational and Applied Mechanics (CERECAM)(计算与应用力学研究中心);Department of Mathematics and Applied Mathematics, University of Cape Town(开普敦大学数学与应用数学系);The African Institute for Mathematical Sciences(AIMS) of South Africa(南非非洲数学科学研究院);Fakultät für Mathematik, Technische Universität Chemnitz(开姆尼茨工业大学数学学院)
Antoine Tambue, Jean Daniel Mukam
备注:
arXiv admin note: text overlap with arXiv:1809.03227
This paper aims to investigate numerical approximation of a general second order non-autonomous semilinear parabolic stochastic partial differential equation (SPDE) driven by multiplicative noise. Numerical approximations of autonomous SPDEs are thoroughly investigated in the literature, while the non-autonomous case is not yet understood. We discretize the non-autonomous SPDE driven by multiplicative noise by the finite element method in space and the Magnus-type integrator in time. We provide a strong convergence proof of the fully discrete scheme toward the mild solution in the root-mean-square $L^2$ norm. The result reveals how the convergence orders in both space and time depend on the regularity of the noise and the initial data. In particular, for multiplicative trace class noise we achieve convergence order $\mathcal{O}\left(h^2\left(1+\max(0,\ln\left(t_m/h^2\right)\right)+Δt^{1/2}\right)$.
Numerical simulations to illustrate our theoretical finding are provided.
A procedure to numerically integrate non-autonomous linear delay differential equations is presented. It is based on the use of an spectral discretization of the delayed part to transform the original problem into a matrix linear ordinary differential equation which is subsequently solved with numerical integrators obtained from the Magnus expansion. The algorithm can be used in the periodic case to get both accurate approximations of the characteristic multipliers and the solution itself. In addition, it can be extended to deal with certain quasilinear delay equations.
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Strong convergence of some Magnus-type schemes for the finite element discretization of non-autonomous parabolic SPDEs driven by additive fractional Brownian motion and Poisson random measure
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High-order integrator for sampling the invariant distribution of a class of parabolic SPDEs with additive space-time noise
Univ Lyon, Université Claude Bernard Lyon 1(里昂大学,克洛德·贝尔纳里昂大学);CNRS UMR 5208, Institut Camille Jordan(法国国家科学研究中心联合研究实验室5208,卡米耶·若当研究所);Université de Genève(日内瓦大学)
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Weak convergence for a stochastic exponential integrator and finite element discretization of SPDE for multiplicative \& additive noise
Stellenbosh University(斯泰伦博斯大学);Center for Research in Computational and Applied Mechanics (CERECAM)(计算与应用力学研究中心);Department of Mathematics and Applied Mathematics, University of Cape Town(开普敦大学数学与应用数学系);School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal(夸祖鲁-纳塔尔大学数学、统计与计算机科学学院)
Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València(瓦伦西亚理工大学多学科数学研究所);Departament de Matemàtiques and IMAC, Universitat Jaume I(海梅一世大学数学系与IMAC);Department of Applied Mathematics and Theoretical Physics, University of Cambridge(剑桥大学应用数学与理论物理系)
Strong convergence of an fractional exponential integrator scheme for the finite element discretization of time-fractional SPDE driven by standard and fractional Brownian motions
We introduce exponential numerical integration methods for stiff stochastic dynamical systems of the form $d\mathbf{z}_t = L(t)\mathbf{z}_tdt + \mathbf{f}(t)dt + Q(t)d\mathbf{W}_t$. We consider the setting of time-varying operators $L(t), Q(t)$ where they may not commute $L(t_1)L(t_2) \neq L(t_2)L(t_1)$, raising challenges for exponentiation. We develop stochastic numerical integration methods using Mangus expansions for preserving statistical structures and for maintaining fluctuation-dissipation balance for physical systems. For computing the contributions of the fluctuation terms, our metho…
Preconditioning for the high-order sampling of the invariant distribution of parabolic semilinear SPDEs
Universite de Pau et des Pays de l’Adour, E2S UPPA, CNRS, LMAP(波城大学、E2S 巴约讷-比亚里茨大学、法国国家科学研究中心、数学与及其应用实验室);Univ Rennes, INRIA (MINGuS), IRMAR - UMR 6625, ENS Rennes(雷恩大学、法国国家信息与自动化研究所(MINGuS)、雷恩数学研究所(UMR 6625)、雷恩高等师范学院);Univ Rennes, CNRS, IRMAR - UMR 6625, ENS Rennes(雷恩大学、法国国家科学研究中心、雷恩数学研究所(UMR 6625)、雷恩高等师范学院);Section of Mathematics, University of Geneva(日内瓦大学数学系)
In this paper, we study the numerical approximation of a general second order semilinear stochastic partial differential equation (SPDE) driven by a additive fractional Brownian motion (fBm) with Hurst parameter $H>\frac 12$ and Poisson random measure, more realistic in modelling real world phenomena. To the best of our knowledge, numerical schemes for such SPDE have been lacked in scientific literature. The approximation is done with the standard finite element method in space and three Euler-type timestepping methods in time, more precisely linear implicit method, exponential integrator and …
Analysis of an exponential integrator for stochastic PDEs driven by Riesz noise
Universite de Pau et des Pays de l’Adour, E2S UPPA(波城大学);Chalmers University of Technology and University of Gothenburg(查尔姆斯理工大学和哥德堡大学);Universitat Autònoma de Barcelona(巴塞罗那自治大学);Swedish Defence Research Agency (FOI)(瑞典国防研究局)
In this paper, we consider the numerical approximation of a general second order semilinear stochastic partial differential equation (SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear part is stronger than the linear part also called stochastic reactive dominated transport equations. Most numerical techniques, including current stochastic exponential integrators lose their good stability properties on such equations. Using finite element for space discretization, we propose a new scheme appropriated on such equations, called stochastic ex…
This paper deals with the numerical approximation of semilinear parabolic stochastic partial differential equation (SPDE) driven simultaneously by Gaussian noise and Poisson random measure, more realistic in modeling real world phenomena. The SPDE is discretized in space with the standard finite element method and in time with the linear implicit Euler method or an exponential integrator, more efficient and stable for stiff problems. We prove the strong convergence of the fully discrete schemes toward the mild solution. The results reveal how convergence orders depend on the regularity of the …
Convergence analysis of the Magnus-Rosenbrock type method for the finite element discretization of semilinear non-autonomous parabolic PDEs with nonsmooth initial data
Western Norway University of Applied Sciences(西挪威应用科学大学);University of Cape Town(开普敦大学);Technische Universität Chemnitz(开姆尼茨工业大学)
We investigate time-adaptive Magnus-type integrators for the numerical approximation of a Mott transistor. The rapidly attenuating electromagnetic field calls for adaptive choice of the time steps. As a basis for step selection, asymptotically correct defect-based estimators of the local error are employed. We analyze the error of the numerical approximation in the presence of the unsmooth external potential and demonstrate the advantages of the adaptive approach.
We introduce a general framework of low regularity integrators which allows us to approximate the time dynamics of a large class of equations, including parabolic and hyperbolic problems, as well as dispersive equations, up to arbitrary high order on general domains. The structure of the local error of the new schemes is driven by nested commutators which in general require (much) lower regularity assumptions than classical methods do. Our main idea lies in embedding the central oscillations of the nonlinear PDE into the numerical discretisation. The latter is achieved by a novel decorated tre…