Results 111 to 120 of about 14,309 (209)

Application of Moving Kriging Shape Functions on Plate Problems

NUST Journal of Engineering Sciences, 2008
The Moving Kriging (MK) interpolation was recently proposed as a superior substitution of the Moving Least Square (MLS) approximation in the construction of shape functions for the Element-Free Galerkin Method (EFGM). Although Kriging is already a very well­ known geo-statistical technique for spatial interpolation in geology and mining, it has only ...
Shazim Ali Memon, Worsak Kanok- Nukulchai, Sajjad Haider   +2 more
openaire   +1 more source

Model Selection for the Mean Function of Kriging Models

Journal of Education and Science
Kriging models are used in many scientific disciplines to investigate the behavior of physical systems. In the Kriging model (KM), the response of the computer simulation code (CSC) is considered to have a Gaussian process (GP). To discover variables influencing responses, choosing a selection of variables or creating a strongly reduced regression ...
Najlaa Sadeek Yahya, Younus Y. Al-Taweel, Luma Ahmad Khaleel   +2 more
openaire   +1 more source

Considerations of Accuracy and Uncertainty with Kriging Surrogate Models in Single-Objective Electromagnetic Design Optimization

, 2007
The high computational cost of evaluating objective functions in electromagnetic optimal design problems necessitates the use of cost-effective techniques.
Hawe, G., Sykulski, J.K.
core  

Reliability-based design optimization of shells with uncertain geometry using adaptive Kriging metamodels

, 2017
Optimal design under uncertainty has gained much attention in the past ten years due to the ever increasing need for manufacturers to build robust systems at the lowest cost.
Bourinet, J. -M., Dubourg, V., Sudret, B.   +2 more
core  

Semi-Parametric Functional Kriging Regression Model with L1 Penalty

Highlights in Science, Engineering and Technology
Partial functional linear models are widely studied and applied models, where the response variable is related to both general random variables and functional random variables. However, with the increasing application of data scenarios involving functional and vector-valued covariates and scalar responses in modern science, this paper proposes a ...
Rui Chen, Zhiyong Zhou
openaire   +1 more source

GAMs and functional kriging for air quality data

, 2016
Data having spatio-temporal structure are often observed in environmental sciences. They may be considered as discrete observations from curves along time and/or space and treated as functional. Generalized Additive Models (GAMs) represent a useful tool for modelling, for example, as pollutant concentrations describing their spatial and/or temporal ...
DI SALVO, Francesca, PLAIA, Antonella, RUGGIERI, Mariantonietta   +2 more
openaire   +1 more source

Estimation of Spatial Coherency Functions for Kriging of Spatial Data

Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 2016
In order to apply Kriging methods for geostatistics of spatial data, an estimation of spatial coherency functions is required priorly based on the spatial distance between measurement points. In the study, the typical coherency functions, such as semi-variogram, homeogram, and covariance function, were estimated using the national geoid model. The test
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Granger causality between energy use and economic growth in France with using geostatistical models [PDF]

This paper introduces a new way for investigating linear and nonlinear Granger causality between energy use and economic growth in France over the period 1960_2005 with using geostatistical models (kiriging and IDW).
Amiri, Arshia, Zibaei, Mansour
core   +1 more source

The influence of correlation functions on stochastic kriging metamodels [PDF]

Proceedings of the 2010 Winter Simulation Conference, 2010
Wei Xie, Barry Nelson, Jeremy Staum
openaire   +1 more source

Radial Basis Functions and Kriging Metamodels for Aerodynamic Optimization

, 2007
Population-based optimization methods like genetic algorithms and particle swarm optimization are very general and robust but can be costly since they require large number of function evaluations. The costly function evaluations can be replaced by cheaper models which are refered to as surrogate or meta models.
Chandrashekarappa, Praveen, Duvigneau, Regis   +1 more
openaire   +1 more source