Results 251 to 260 of about 2,125 (287)

Collocation least-squares polynomial chaos method

Proceedings of the 2010 Spring Simulation Multiconference, 2010
The polynomial chaos (PC) method has been used in many engineering applications to replace the traditional Monte Carlo (MC) approach for uncertainty quantification (UQ) due to its better convergence properties. Many researchers seek to further improve the efficiency of PC, especially in higher dimensional space with more uncertainties. The intrusive PC
Haiyan Cheng, Adrian Sandu
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Least squares collocation and statistical testing

Bulletin Géodésique, 1990
The authors address the issue of statistical testing in least squares collocation (LSC) in two stages. The first stage concerns the extension and focusing of theLSC equations to the task of statistical testing. The second stage deals with statistical testing titself and is introduced in the second portion of the paper.
Edward J. Krakiwsky, Zoltán F. Biacs
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LEAST-SQUARES MESHFREE COLLOCATION METHOD

International Journal of Computational Methods, 2012
A least-squares meshfree collocation method is presented. The method is based on the first-order differential equations in order to result in a better conditioned linear algebraic equations, and to obtain the primary variables (displacements) and the dual variables (stresses) simultaneously with the same accuracy.
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Least squares adjustment and collocation

Bulletin Géodésique, 1977
For the estimation of parameters in linear models best linear unbiased estimates are derived in case the parameters are random variables. If their expected values are unknown, the well known formulas of least squares adjustment are obtained. If the expected values of the parameters are known, least squares collocation, prediction and filtering are ...
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AN INTRODUCTION TO LEAST-SQUARES COLLOCATION

Survey Review, 1987
AbstractThis paper proyides an introduction to least-squares collocation, a process already well established in statistical geodesy for prediction, filtering and modelling. Variance/covariance propagation laws are shown to have hitherto-unnoticed applications including prediction and filtering formulae usually obtained by least-squares criteria.
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Collocation and Least Squares Methods

2011
Various methods to adjust given or chosen mathematical models to actual requirements or actual data are presented. Such adjustment, if it exists, means choice of proper values of some parameters, which usually leads to solution of a set of linear or nonlinear equations. When such adjustment is not possible then least squares method gives an alternative.
Jiří Gregor, Jaroslav Tišer
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Development of the collocations and least squares method

Proceedings of the Steklov Institute of Mathematics, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Isaev, V. I., Shapeev, V. P.
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Least‐squares spectral collocation method for the Stokes equations

Numerical Methods for Partial Differential Equations, 2003
AbstractFirst‐order system least‐squares spectral collocation methods are presented for the Stokes equations by adopting the first‐order system and modifying the least‐squares functionals in 2. Then homogeneous Legendre and Chebyshev (continuous and discrete) functionals are shown to be elliptic and continuous with respect to appropriate product ...
Kim, Sang Dong, Lee, Hyung-Chun, Shin, Byeong Chun   +2 more
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Transformation of coordinates using least squares collocation

Australian Surveyor, 1994
Abstract This paper presents three methods of transforming World Geodetic System 1984 (WGS84) coordinates to Australian Geodetic Datum 1966 (AGD66) coordinates. Traditional methods of scaling, translating and rotating are compared with the least squares collocation technique of parameter estimation, filtering and interpolation revealing some advantages
R. E. Deakin, F. J. Leahy
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Comparison of kriging and least-squares collocation – Revisited

Journal of Applied Geodesy, 2022
Abstract The well-known to physical geodesists method of least-squares collocation and the geostatistical method of kriging probably known to the broader audience are compared. Both methods are rooted in Wiener–Kolmogorov’s (W–K) prediction theory; but, since necessity is the mother of invention, the W–K foundations have been extended to
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