Radial basis function and multi-level 2D vector field approximation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43960097" target="_blank" >RIV/49777513:23520/21:43960097 - isvavai.cz</a>

Výsledek na webu

<a href="http://afrodita.zcu.cz/~skala/publications.htm" target="_blank" >http://afrodita.zcu.cz/~skala/publications.htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.matcom.2020.10.009" target="_blank" >10.1016/j.matcom.2020.10.009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Radial basis function and multi-level 2D vector field approximation

Popis výsledku v původním jazyce

We propose a new approach for meshless multi-level radial basis function (ML-RBF) approximation which offers data-sensitive compression and progressive details visualization. It leads to an analytical description of compressed vector fields, too. The proposed approach approximates the vector field at multiple levels of detail. The low-level approximation removes minor flow patterns while the global character of the flow remains unchanged. And conversely, the higher level approximation contains all small details of the vector field. The ML-RBF has been tested with a numerical forecast data set and 3D tornado data set to prove its ability to handle data with complex topology. Comparison with the Fourier vector field approximation has been made and significant advantages, i.e. high compression ratio, accuracy, extensibility to a higher dimension etc., of the proposed ML-RBF were proved.

Název v anglickém jazyce

Radial basis function and multi-level 2D vector field approximation

Popis výsledku anglicky

We propose a new approach for meshless multi-level radial basis function (ML-RBF) approximation which offers data-sensitive compression and progressive details visualization. It leads to an analytical description of compressed vector fields, too. The proposed approach approximates the vector field at multiple levels of detail. The low-level approximation removes minor flow patterns while the global character of the flow remains unchanged. And conversely, the higher level approximation contains all small details of the vector field. The ML-RBF has been tested with a numerical forecast data set and 3D tornado data set to prove its ability to handle data with complex topology. Comparison with the Fourier vector field approximation has been made and significant advantages, i.e. high compression ratio, accuracy, extensibility to a higher dimension etc., of the proposed ML-RBF were proved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless metody pro vizualizaci velkých časově-prostorových vektorových dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Mathematics and Computers in Simulation

ISSN

0378-4754

e-ISSN

—

Svazek periodika

181

Číslo periodika v rámci svazku

MAR 2021

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

522-538

Kód UT WoS článku

000585853800028

EID výsledku v databázi Scopus

2-s2.0-85094326169