Multiscale Methods

By M. Ethods, Enn Saar. 2006

181 citations recorded by the source corpus.

The lecture introduces a trous wavelet transforms, explains how to estimate the spatial density for galaxy distributions, and, finally, how to describe the morphology of cosmological density fields. An example application of these methods to the 2dFGRS gives unexpected results.

Subfields: Artificial Intelligence, Geophysics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b0b48330099b58849aee0da12d4979b288335b85

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. S. Armstrong

In Statistical and Nonlinear Physics

1. David Kelly
2. Jean-Christophe Mourrat
3. Ian Melbourne
4. Tuomo Kuusi
5. S. Armstrong
6. Georg A. Gottwald
7. Charalampos Skokos
8. Giuseppe Mingione

Citations in the corpus

• Smooth approximation of stochastic differential equations (2014, background): “) In applications, smooth processes Wn that approximate Brownian motion arise naturally from differential equations as follows [18, 21, 35, 41, 42].”
• Quantitative Stochastic Homogenization and Large-Scale Regularity (2017, background): “There is a vast and rich mathematical literature on homogenization developed in the last forty years and already many good expositions on the topic (see for instance the books [5, 22, 27, 33, 34, 74, 80, 105, 113]).”