57 research outputs found
A multiscale method for heterogeneous bulk-surface coupling
In this paper, we construct and analyze a multiscale (finite element) method
for parabolic problems with heterogeneous dynamic boundary conditions. As
origin, we consider a reformulation of the system in order to decouple the
discretization of bulk and surface dynamics. This allows us to combine
multiscale methods on the boundary with standard Lagrangian schemes in the
interior. We prove convergence and quantify explicit rates for low-regularity
solutions, independent of the oscillatory behavior of the heterogeneities. As a
result, coarse discretization parameters, which do not resolve the fine scales,
can be considered. The theoretical findings are justified by a number of
numerical experiments including dynamic boundary conditions with random
diffusion coefficients
Localized Orthogonal Decomposition for two-scale Helmholtz-type problems
In this paper, we present a Localized Orthogonal Decomposition (LOD) in
Petrov-Galerkin formulation for a two-scale Helmholtz-type problem. The
two-scale problem is, for instance, motivated from the homogenization of the
Helmholtz equation with high contrast, studied together with a corresponding
multiscale method in (Ohlberger, Verf\"urth. A new Heterogeneous Multiscale
Method for the Helmholtz equation with high contrast, arXiv:1605.03400, 2016).
There, an unavoidable resolution condition on the mesh sizes in terms of the
wave number has been observed, which is known as "pollution effect" in the
finite element literature. Following ideas of (Gallistl, Peterseim. Comput.
Methods Appl. Mech. Engrg. 295:1-17, 2015), we use standard finite element
functions for the trial space, whereas the test functions are enriched by
solutions of subscale problems (solved on a finer grid) on local patches.
Provided that the oversampling parameter , which indicates the size of the
patches, is coupled logarithmically to the wave number, we obtain a
quasi-optimal method under a reasonable resolution of a few degrees of freedom
per wave length, thus overcoming the pollution effect. In the two-scale
setting, the main challenges for the LOD lie in the coupling of the function
spaces and in the periodic boundary conditions.Comment: 20 page
Numerical homogenization for nonlinear strongly monotone problems
In this work we introduce and analyze a new multiscale method for strongly nonlinear monotone equations in the spirit of the Localized Orthogonal Decomposition. A problem-adapted multiscale space is constructed by solving linear local fine-scale problems which is then used in a generalized finite element method. The linearity of the fine-scale problems allows their localization and, moreover, makes the method very efficient to use. The new method gives optimal a priori error estimates up to linearization errors beyond periodicity and scale separation and without assuming higher regularity of the solution. The effect of different linearization strategies is discussed in theory and practice. Several numerical examples including stationary Richards equation confirm the theory and underline the applicability of the method
Higher-order finite element methods for the nonlinear Helmholtz equation
In this work, we analyze the finite element method with arbitrary but fixed polynomial degree for the nonlinear Helmholtz equation with impedance boundary conditions. We show well-posedness and (pre-asymptotic) error estimates of the finite element solution under a resolution condition between the wave number , the mesh size and the polynomial degree p of the form “ sufficiently small” and a so-called smallness of the data assumption. For the latter, we prove that the logarithmic dependence in from the case in [H. Wu, J. Zou, SIAM J. Numer. Anal. 56(3): 1338-1359, 2018] can be removed for . We show convergence of two different fixed-point iteration schemes. Numerical experiments illustrate our theoretical results and compare the robustness of the iteration schemes with respect to the size of the nonlinearity and the right-hand side data
Numerical homogenization for nonlinear strongly monotone problems
In this work we introduce and analyze a new multiscale method for strongly
nonlinear monotone equations in the spirit of the Localized Orthogonal
Decomposition. A problem-adapted multiscale space is constructed by solving
linear local fine-scale problems which is then used in a generalized finite
element method. The linearity of the fine-scale problems allows their
localization and, moreover, makes the method very efficient to use. The new
method gives optimal a priori error estimates up to linearization errors. The
results neither require structural assumptions on the coefficient such as
periodicity or scale separation nor higher regularity of the solution. The
effect of different linearization strategies is discussed in theory and
practice. Several numerical examples including stationary Richards equation
confirm the theory and underline the applicability of the method
Numerical homogenization of H(curl)-problems
If an elliptic differential operator associated with an
-problem involves rough (rapidly varying)
coefficients, then solutions to the corresponding
-problem admit typically very low regularity, which
leads to arbitrarily bad convergence rates for conventional numerical schemes.
The goal of this paper is to show that the missing regularity can be
compensated through a corrector operator. More precisely, we consider the
lowest order N\'ed\'elec finite element space and show the existence of a
linear corrector operator with four central properties: it is computable,
-stable, quasi-local and allows for a correction of
coarse finite element functions so that first-order estimates (in terms of the
coarse mesh-size) in the norm are obtained provided
the right-hand side belongs to . With these four
properties, a practical application is to construct generalized finite element
spaces which can be straightforwardly used in a Galerkin method. In particular,
this characterizes a homogenized solution and a first order corrector,
including corresponding quantitative error estimates without the requirement of
scale separation
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