Scattered Data Fitting with Bivariate Splines

2002 ◽  
pp. 243-286 ◽  
Author(s):  
Frank Zeilfelder
Keyword(s):  
Data Fitting ◽  
Scattered Data ◽  
Bivariate Splines ◽  
Scattered Data Fitting

Local hybrid approximation for scattered data fitting with bivariate splines

2006 ◽  
Vol 23 (9) ◽  
pp. 703-721 ◽  
Author(s):  
Oleg Davydov ◽  
Rossana Morandi ◽  
Alessandra Sestini
Keyword(s):  
Data Fitting ◽  
Scattered Data ◽  
Bivariate Splines ◽  
Scattered Data Fitting ◽  
Hybrid Approximation

Scattered data fitting with simplex splines in two and three dimensional spaces

1997 ◽  
Vol 13 (7) ◽  
pp. 295-315 ◽  
Author(s):  
Jingfang Zhou ◽  
Nicholas M. Patrikalakis ◽  
Seamus T. Tuohy ◽  
Xiuzi Ye
Keyword(s):  
Three Dimensional ◽  
Data Fitting ◽  
Scattered Data ◽  
Scattered Data Fitting ◽  
Simplex Splines

Large scale least squares scattered data fitting

2003 ◽  
Vol 44 (1-2) ◽  
pp. 225-239 ◽  
Author(s):  
V. Pereyra ◽  
G. Scherer
Keyword(s):  
Least Squares ◽  
Large Scale ◽  
Data Fitting ◽  
Scattered Data ◽  
Scattered Data Fitting

Scattered data fitting by minimal surface

2017 ◽  
Vol 25 (3) ◽  
Author(s):  
Yong-Xia Hao ◽  
Dianchen Lu
Keyword(s):  
Tikhonov Regularization ◽  
Numerical Experiments ◽  
Data Fitting ◽  
Scattered Data ◽  
B Spline ◽  
Scattered Data Fitting ◽  
Area Minimization ◽  
Regularization Parameters ◽  
The Given ◽  
Minimal Area

AbstractThe goal of this paper is to develop a computational model for obtaining the fitting surface to the given scattered data with minimal area. The basic idea of the model is to utilize the B-spline and area minimization. The model is turned into a Tikhonov regularization model finally. By choosing the regularization parameters with the L-curve criterion and the GCV method, respectively, numerical experiments indicate that the model can provide an acceptable compromise between the minimization of the data mismatch term and the area of the surface.

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