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Piecewise polynomial interpolation in Muckenhoupt weighted Sobolev spaces and applications
We develop a constructive piecewise polynomial approximation theory in
weighted Sobolev spaces with Muckenhoupt weights for any polynomial degree. The
main ingredients to derive optimal error estimates for an averaged Taylor
polynomial are a suitable weighted Poincare inequality, a cancellation property
and a simple induction argument. We also construct a quasi-interpolation
operator, built on local averages over stars, which is well defined for
functions in . We derive optimal error estimates for any polynomial degree
on simplicial shape regular meshes. On rectangular meshes, these estimates are
valid under the condition that neighboring elements have comparable size, which
yields optimal anisotropic error estimates over -rectangular domains. The
interpolation theory extends to cases when the error and function regularity
require different weights. We conclude with three applications: nonuniform
elliptic boundary value problems, elliptic problems with singular sources, and
fractional powers of elliptic operators
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Approximation of high-dimensional parametric PDEs
Parametrized families of PDEs arise in various contexts such as inverse
problems, control and optimization, risk assessment, and uncertainty
quantification. In most of these applications, the number of parameters is
large or perhaps even infinite. Thus, the development of numerical methods for
these parametric problems is faced with the possible curse of dimensionality.
This article is directed at (i) identifying and understanding which properties
of parametric equations allow one to avoid this curse and (ii) developing and
analyzing effective numerical methodd which fully exploit these properties and,
in turn, are immune to the growth in dimensionality. The first part of this
article studies the smoothness and approximability of the solution map, that
is, the map where is the parameter value and is the
corresponding solution to the PDE. It is shown that for many relevant
parametric PDEs, the parametric smoothness of this map is typically holomorphic
and also highly anisotropic in that the relevant parameters are of widely
varying importance in describing the solution. These two properties are then
exploited to establish convergence rates of -term approximations to the
solution map for which each term is separable in the parametric and physical
variables. These results reveal that, at least on a theoretical level, the
solution map can be well approximated by discretizations of moderate
complexity, thereby showing how the curse of dimensionality is broken. This
theoretical analysis is carried out through concepts of approximation theory
such as best -term approximation, sparsity, and -widths. These notions
determine a priori the best possible performance of numerical methods and thus
serve as a benchmark for concrete algorithms. The second part of this article
turns to the development of numerical algorithms based on the theoretically
established sparse separable approximations. The numerical methods studied fall
into two general categories. The first uses polynomial expansions in terms of
the parameters to approximate the solution map. The second one searches for
suitable low dimensional spaces for simultaneously approximating all members of
the parametric family. The numerical implementation of these approaches is
carried out through adaptive and greedy algorithms. An a priori analysis of the
performance of these algorithms establishes how well they meet the theoretical
benchmarks
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