1,192 research outputs found
Wavelets and their use
This review paper is intended to give a useful guide for those who want to
apply discrete wavelets in their practice. The notion of wavelets and their use
in practical computing and various applications are briefly described, but
rigorous proofs of mathematical statements are omitted, and the reader is just
referred to corresponding literature. The multiresolution analysis and fast
wavelet transform became a standard procedure for dealing with discrete
wavelets. The proper choice of a wavelet and use of nonstandard matrix
multiplication are often crucial for achievement of a goal. Analysis of various
functions with the help of wavelets allows to reveal fractal structures,
singularities etc. Wavelet transform of operator expressions helps solve some
equations. In practical applications one deals often with the discretized
functions, and the problem of stability of wavelet transform and corresponding
numerical algorithms becomes important. After discussing all these topics we
turn to practical applications of the wavelet machinery. They are so numerous
that we have to limit ourselves by some examples only. The authors would be
grateful for any comments which improve this review paper and move us closer to
the goal proclaimed in the first phrase of the abstract.Comment: 63 pages with 22 ps-figures, to be published in Physics-Uspekh
Wavelet-based density estimation for noise reduction in plasma simulations using particles
For given computational resources, the accuracy of plasma simulations using
particles is mainly held back by the noise due to limited statistical sampling
in the reconstruction of the particle distribution function. A method based on
wavelet analysis is proposed and tested to reduce this noise. The method, known
as wavelet based density estimation (WBDE), was previously introduced in the
statistical literature to estimate probability densities given a finite number
of independent measurements. Its novel application to plasma simulations can be
viewed as a natural extension of the finite size particles (FSP) approach, with
the advantage of estimating more accurately distribution functions that have
localized sharp features. The proposed method preserves the moments of the
particle distribution function to a good level of accuracy, has no constraints
on the dimensionality of the system, does not require an a priori selection of
a global smoothing scale, and its able to adapt locally to the smoothness of
the density based on the given discrete particle data. Most importantly, the
computational cost of the denoising stage is of the same order as one time step
of a FSP simulation. The method is compared with a recently proposed proper
orthogonal decomposition based method, and it is tested with three particle
data sets that involve different levels of collisionality and interaction with
external and self-consistent fields
Wavelets and Fast Numerical Algorithms
Wavelet based algorithms in numerical analysis are similar to other transform
methods in that vectors and operators are expanded into a basis and the
computations take place in this new system of coordinates. However, due to the
recursive definition of wavelets, their controllable localization in both space
and wave number (time and frequency) domains, and the vanishing moments
property, wavelet based algorithms exhibit new and important properties.
For example, the multiresolution structure of the wavelet expansions brings
about an efficient organization of transformations on a given scale and of
interactions between different neighbouring scales. Moreover, wide classes of
operators which naively would require a full (dense) matrix for their numerical
description, have sparse representations in wavelet bases. For these operators
sparse representations lead to fast numerical algorithms, and thus address a
critical numerical issue.
We note that wavelet based algorithms provide a systematic generalization of
the Fast Multipole Method (FMM) and its descendents.
These topics will be the subject of the lecture. Starting from the notion of
multiresolution analysis, we will consider the so-called non-standard form
(which achieves decoupling among the scales) and the associated fast numerical
algorithms. Examples of non-standard forms of several basic operators (e.g.
derivatives) will be computed explicitly.Comment: 32 pages, uuencoded tar-compressed LaTeX file. Uses epsf.sty (see
`macros'
- …