124 research outputs found
On a multiwavelet spectral element method for integral equation of a generalized Cauchy problem
In this paper we deal with construction and analysis of a multiwavelet spectral element scheme for a generalized Cauchy type problem with Caputo fractional derivative. Numerical schemes for this type of problems, often suffer from the draw-back of spurious oscillations. A common remedy is to render the problem to an equivalent integral equation. For the generalized Cauchy type problem, a corresponding integral equation is of nonlinear Volterra type. In this paper we investigate wellposedness and convergence of a stabilizing multiwavelet scheme for a, one-dimensional case (in [a,\ua0b] or [0,\ua01]), of this problem. Based on multiwavelets, we construct an approximation procedure for the fractional integral operator that yields a linear system of equations with sparse coefficient matrix. In this setting, choosing an appropriate threshold, the number of non-zero coefficients in the system is substantially reduced. A severe obstacle in the convergence analysis is the lack of continuous derivatives in the vicinity of the inflow/ starting boundary point. We overcome this issue through separating a J (mesh)-dependent, small, neighborhood of a (or origin) from the interval, where we only take L2-norm. The estimate in this part relies on Chebyshev polynomials, viz. As reported by Richardson(Chebyshev interpolation for functions with endpoint singularities via exponential and double-exponential transforms, Oxford University, UK, 2012) and decreases, almost, exponentially by raising J. At the remaining part of the domain the solution is sufficiently regular to derive the desired optimal error bound. We construct such a modified scheme and analyze its wellposedness, efficiency and accuracy. The robustness of the proposed scheme is confirmed implementing numerical examples
Construction of interpolating and orthonormal multigenerators and multiwavelets on the interval
In den letzten Jahren haben sich Wavelets zu einem hochwertigen Hilfsmittel in der angewandten
Mathematik entwickelt. Eine Waveletbasis ist im Allgemeinen ein System von
Funktionen, das durch die Skalierung, Translation und Dilatation einer endlichen Menge
von Funktionen, den sogenannten Mutterwavelets, entsteht. Wavelets wurden sehr erfolgreich
in der digitalen Signal- und Bildanalyse, z. B. zur Datenkompression verwendet.
Ein weiteres wichtiges Anwendungsfeld ist die Analyse und die numerische Behandlung
von Operatorgleichungen. Insbesondere ist es gelungen, adaptive numerische Algorithmen
basierend auf Wavelets für eine riesige Klasse von Operatorgleichungen, einschließlich
Operatoren mit negativer Ordnung, zu entwickeln. Der Erfolg der Wavelet-
Algorithmen ergibt sich als Konsequenz der folgenden Fakten:
- Gewichtete Folgennormen von Wavelet-Expansionskoeffizienten sind in einem bestimmten
Bereich (abhängig von der Regularität der Wavelets) äquivalent zu
Glättungsnormen wie Besov- oder Sobolev-Normen.
- Für eine breite Klasse von Operatoren ist ihre Darstellung in Wavelet-Koordinaten
nahezu diagonal.
- Die verschwindenden Momente von Wavelets entfernen den glatten Teil einer Funktion
und führen zu sehr effizienten Komprimierungsstrategien.
Diese Fakten können z. B. verwendet werden, um adaptive numerische Strategien mit
optimaler Konvergenzgeschwindigkeit zu konstruieren, in dem Sinne, dass diese Algorithmen
die Konvergenzordnung der besten N-Term-Approximationsschemata realisieren.
Die maßgeblichen Ergebnisse lassen sich für lineare, symmetrische, elliptische Operatorgleichungen
erzielen. Es existiert auch eine Verallgemeinerung für nichtlineare elliptische
Gleichungen. Hier verbirgt sich jedoch eine ernste Schwierigkeit: Jeder numerische Algorithmus
für diese Gleichungen erfordert die Auswertung eines nichtlinearen Funktionals,
welches auf eine Wavelet-Reihe angewendet wird. Obwohl einige sehr ausgefeilte Algorithmen
existieren, erweisen sie sich als ziemlich langsam in der Praxis. In neueren Studien
wurde gezeigt, dass dieses Problem durch sogenannte Interpolanten verbessert werden
kann. Dabei stellt sich heraus, dass die meisten bekannten Basen der Interpolanten
keine stabilen Basen in L2[a,b] bilden.
In der vorliegenden Arbeit leisten wir einen wesentlichen Beitrag zu diesem Problem
und konstruieren neue Familien von Interpolanten auf beschränkten Gebieten, die nicht
nur interpolierend, sondern auch stabil in L2[a,b] sind. Da dies mit nur einem Generator
schwer (oder vielleicht sogar unmöglich) zu erreichen ist, werden wir mit Multigeneratoren
und Multiwavelets arbeiten.In recent years, wavelets have become a very powerful tools in applied
mathematics. In general,
a wavelet basis is a system of functions that is generated by scaling, translating and dilating a
finite set of functions, the so-called mother wavelets. Wavelets have been very successfully
applied in image/signal analysis, e.g., for denoising and compression purposes. Another
important field of applications is the analysis and the numerical treatment of operator
equations. In particular, it has been possible to design adaptive numerical algorithms based on
wavelets for a huge class of operator equations including operators of negative order. The
success of wavelet algorithms is an ultimative consequence of the following facts:
- Weighted sequence norms of wavelet expansion coefficients are equivalent in a certain
range (depending on the regularity of the wavelets) to smoothness norms such as Besov
or Sobolev norms.
- For a wide class of operators their representation in wavelet coordinates is nearly
diagonal.
-The vanishing moments of wavelets remove the smooth part of a function.
These facts can,
e.g., be used to construct adaptive numerical strategies that are guaranteed to
converge with optimal order, in the sense that these algorithms realize the convergence order
of best N-term approximation schemes. The most far-reaching results have been obtained for
linear, symmetric elliptic operator equations. Generalization to nonlinear elliptic equations also
exist. However, then one is faced with a serious bottleneck: every numerical algorithm for these
equations requires the evaluation of a nonlinear functional applied to a wavelet series.
Although some very sophisticated algorithms exist, they turn out to perform quite slowly in
practice. In recent studies, it has been shown that this problem can be ameliorated by means of
so called interpolants. However, then the problem occurs that most of the known bases of
interpolants do not form stable bases in L2[a,b].
In this PhD project, we intend to provide a significant
contribution to this problem. We want to
construct new families of interpolants on domains that are not only interpolating, but also
stable in L2[a,b]or even orthogonal. Since this is hard to achieve (or maybe even impossible)
with just one generator, we worked with multigenerators and multiwavelets
Adaptive sparse grid discontinuous Galerkin method: review and software implementation
This paper reviews the adaptive sparse grid discontinuous Galerkin (aSG-DG)
method for computing high dimensional partial differential equations (PDEs) and
its software implementation. The C\texttt{++} software package called AdaM-DG,
implementing the aSG-DG method, is available on Github at
\url{https://github.com/JuntaoHuang/adaptive-multiresolution-DG}. The package
is capable of treating a large class of high dimensional linear and nonlinear
PDEs. We review the essential components of the algorithm and the functionality
of the software, including the multiwavelets used, assembling of bilinear
operators, fast matrix-vector product for data with hierarchical structures. We
further demonstrate the performance of the package by reporting numerical error
and CPU cost for several benchmark test, including linear transport equations,
wave equations and Hamilton-Jacobi equations
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