![]() ![]() See also Wikimedia Commons has media related to. Kriging is therefore used very often as a so-called, implemented inside routines. It is therefore more efficient to design and run a limited number of computer simulations, and then use a kriging interpolator to rapidly predict the response in any other design point. In many practical engineering problems, such as the design of a process, a single FEM simulation might be several hours or even a few days long. A black box model built over a designed set of. ![]() In this case, kriging is used as a tool, i.e. It can be used where spatially-related data has been collected (in 2-D or 3-D) and estimates of 'fill-in' data are desired in the locations (spatial gaps) between the actual measurements.To date kriging has been used in a variety of disciplines, including the following.Design and analysis of computer experiments Another very important and rapidly growing field of application, in, is the interpolation of data coming out as response variables of deterministic computer simulations, e.g. This section is very poor and needs to be improvedAlthough kriging was developed originally for applications in geostatistics, it is a general method of statistical interpolation that can be applied within any discipline to sampled data from random fields that satisfy the appropriate mathematical assumptions. The word is sometimes capitalized as Kriging in the literature. The English verb is to krige and the most common noun is kriging both are often pronounced with a, following the pronunciation of the name 'Krige'. Krige sought to estimate the most likely distribution of gold based on samples from a few boreholes. The dashed curve shows a spline that is smooth, but departs significantly from the expected intermediate values given by those means.The theoretical basis for the method was developed by the French mathematician in 1960, based on the Master's thesis of, the pioneering plotter of distance-weighted average gold grades at the reef complex in. ![]() The kriging interpolation, shown in red, runs along the means of the normally distributed confidence intervals shown in gray. Squares indicate the location of the data. Example of one-dimensional data interpolation by kriging, with confidence intervals. ![]()
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