Finding points of importance for radial basis function approximation of large scattered data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43961926" target="_blank" >RIV/49777513:23520/20:43961926 - isvavai.cz</a>

Result on the web

<a href="https://www.springer.com/series/558" target="_blank" >https://www.springer.com/series/558</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-50433-5_19" target="_blank" >10.1007/978-3-030-50433-5_19</a>

Result language

angličtina

Original language name

Finding points of importance for radial basis function approximation of large scattered data

Original language description

Interpolation and approximation methods are used in many fields such as in engineering as well as other disciplines for various scientific discoveries. If the data domain is formed by scattered data, approximation methods may become very complicated as well as time-consuming. Usually, the given data is tessellated by some method, not necessarily the Delaunay triangulation, to produce triangular or tetrahedral meshes. After that approximation methods can be used to produce the surface. However, it is difficult to ensure the continuity and smoothness of the final interpolant along with all adjacent triangles. In this contribution, a meshless approach is proposed by using radial basis functions (RBFs). It is applicable to explicit functions of two variables and it is suitable for all types of scattered data in general. The key point for the RBF approximation is finding the important points that give a good approximation with high precision to the scattered data. Since the compactly supported RBFs (CSRBF) has limited influence in numerical computation, large data sets can be processed efficiently as well as very fast via some efficient algorithm. The main advantage of the RBF is, that it leads to a solution of a system of linear equations (SLE) Ax = b. Thus any efficient method solves the systems of linear equations that can be used. In this study is we propose a new method of determining the importance points on the scattered data that produces a very good reconstructed surface with higher accuracy while maintaining the smoothness of the surface.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

<a href="/en/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless methods for large scattered spatio-temporal vector data visualization</a><br>

Continuities

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

Publication year

2020

Confidentiality

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

Article name in the collection

ICCS 2020

ISBN

978-3-030-50432-8

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

11

Pages from-to

239-250

Publisher name

Springer

Place of publication

Cham

Event location

Amsterdam, The Netherlands

Event date

Jun 3, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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