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Publication# Numerical methods for deterministic and stochastic differential equations with multiple scales and high contrasts

Abstract

Mathematical models involving multiple scales are essential for the description of physical systems. In particular, these models are important for the simulation of time-dependent phenomena, such as the heat flow, where the Laplacian contains mixed and indistinguishable fast and slow modes. Stationary problems can also exhibit a multiscale nature. For example, elliptic equations governed by a diffusion coefficient with strong discontinuities have solutions characterized by regions with a high gradient. Simulating such models is very demanding, as the computational cost of standard numerical methods is usually ruled by the fastest dynamics or the smallest scale.

In the first part of this thesis, we develop multirate integration methods for deterministic and stochastic time-dependent problems with disparate time-scales. The cost of traditional schemes for such problems is prohibitive due to step size restrictions in the explicit case or solutions to large nonlinear systems in the implicit case. Existing multirate methods are either implicit or make use of interpolations, which trigger instabilities, or are based on a scale separation assumption, which is not satisfied by parabolic problems. Here we introduce a new framework based on modified equations which allows for the development of a whole new class of interpolation-free explicit multirate numerical methods, which do not need any scale separation, are stable and accurate. For deterministic problems, our methodology is based on the replacement of the original right-hand side by an averaged force, whose stiffness is reduced due to a fast but cheap auxiliary problem. Integrating the modified equation and the auxiliary problems by explicit schemes is generally cheaper than integrating the original problem. We thus introduce a multirate method based on stabilized explicit schemes and prove its efficiency, stability and accuracy. Numerical experiments show that standard schemes and our multirate approach provide essentially the same solutions; hence, the bottleneck caused by the stiffness of a few degrees of freedom is overcome without sacrificing accuracy. We also generalize the same framework to stochastic differential equations, where we need to introduce a damped diffusion term for which the resulting modified equation inherits the mean-square stability properties of the original problem. An interpolation-free stabilized explicit multirate method for stochastic equations is then derived.

In the second part of this thesis, we consider elliptic problems with high gradients and develop a local adaptive discontinuous Galerkin scheme. Local methods for such problems already exist in literature; however, they are usually based on iterations and have several downsides. In particular, their a priori error analysis is based on rather strong and nonphysical assumptions and they lack a rigorous a posteriori error analysis. The scheme that we propose is based on a coarse solution on the full domain which is subsequently improved by solving local elliptic problems only once on subdomains with artificial boundary conditions. The a priori error analysis is performed under minimal regularity assumptions due to the gradient discretization framework. Furthermore, we derive a posteriori error estimators based on conforming fluxes and potential reconstructions which can be used to identify the local subdomains on the fly, are free of undetermined constants and robust in singularly perturbed regimes.

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Numerical methods for partial differential equations

Numerical methods for partial differential equations is the branch of numerical analysis that studies the numerical solution of partial differential equations (PDEs). In principle, specialized methods for hyperbolic, parabolic or elliptic partial differential equations exist. Finite difference method In this method, functions are represented by their values at certain grid points and derivatives are approximated through differences in these values.

Stochastic differential equation

A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution which is also a stochastic process. SDEs have many applications throughout pure mathematics and are used to model various behaviours of stochastic models such as stock prices, random growth models or physical systems that are subjected to thermal fluctuations. SDEs have a random differential that is in the most basic case random white noise calculated as the derivative of a Brownian motion or more generally a semimartingale.

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals. Many differential equations cannot be solved exactly. For practical purposes, however – such as in engineering – a numeric approximation to the solution is often sufficient. The algorithms studied here can be used to compute such an approximation.

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