- Referenced in 294 articles
- spatial: Functions for Kriging and Point Pattern Analysis , Functions for kriging and point pattern analysis...
- Referenced in 130 articles
- Matlab toolbox for working with kriging approximations to computer models. Typical use of this software ... construct a kriging approximation model based on data from a computer experiment ... evaluate the computer model for constructing the kriging approximation...
- Referenced in 63 articles
- CART and random forest; Gaussian process models (Kriging), and combinations of di erent metamodeling approaches...
- Referenced in 63 articles
- package FRK. Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling and prediction with large...
- Referenced in 37 articles
- package DiceKriging: Kriging methods for computer experiments. Estimation, validation and prediction of kriging models. Important...
- Referenced in 33 articles
- methods include cubic, and thin plate splines, Kriging and compact covariances for large data sets ... splines and Kriging methods are supporting by functions that can determine the smoothing parameter (nugget...
- Referenced in 32 articles
- package DiceOptim: Kriging-based optimization for computer experiments. Expected Improvement. EGO algorithm. Multipoints ... Constant Liars. Criteria and algorithms for Noisy Kriging-based Optimization , including...
- Referenced in 41 articles
- more common geostatistics algorithms, such as kriging of one or more variables, sequential and multiple...
- Referenced in 35 articles
- extreme value random fields; conditional simulation; kriging; maximum likelihood estimation...
- Referenced in 30 articles
- ordinary and universal point or block (co)kriging, sequential Gaussian or indicator (co)simulation; variogram...
- Referenced in 15 articles
- models (anisotropy coefficients are not fitted automatically). Kriging and (sequential) conditional simulation are done under ... this model include ordinary and simple kriging, ordinary or simple cokriging, universal kriging, external drift ... kriging, Gaussian conditional or unconditional simulation or cosimulation. In addition, variables may share trend coefficients...
- Referenced in 12 articles
- active learning reliability method combining Kriging and Monte Carlo Simulation. An important challenge in structural ... researchers in the last decades. However, recently, Kriging, originated from geostatistics, have emerged in reliability ... analysis. Widespread in optimisation, Kriging has just started to appear in uncertainty propagation ... approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures...
- Referenced in 18 articles
- devoted to techniques that are known as kriging. The third part presents the fully general...
- Referenced in 10 articles
- mesh adaptive direct search (MADS) combined with kriging. The results show that the AQUARS methods ... MLSL coupled to MADS with kriging on the watershed calibration problem...
- Referenced in 13 articles
- design, D-optimal and maxmin designs. Surrogates: kriging, polynomial response surface, radial basis neural network...
- Referenced in 8 articles
- package KrigInv: Kriging-based Inversion for Deterministic and Noisy Computer Experiments. Criteria and algorithms...
- Referenced in 3 articles
- ooDACE toolbox: a flexible object-oriented Kriging implementation. When analyzing data from computationally expensive simulation ... space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used ... Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensive research ... many extensions have been proposed, e.g., co-Kriging, stochastic Kriging, blind Kriging, etc. However...
- Referenced in 7 articles
- polynomial chaos expansions, Gaussian process modelling, a.k.a. Kriging, low-rank tensor approximations), rare event estimation...
- Referenced in 6 articles
- linear models, additive models, MARS,PolyMARS and Kriging...
- Referenced in 4 articles
- package geofd which implements ordinary kriging prediction for this type of data. Initially the curves ... parameters for performing prediction by ordinary kriging at unsampled locations are by estimated solving...