Results 11 to 20 of about 10,081 (210)
The quasi-fiducial model selection for Kriging model
Statistical Theory and Related FieldsKriging models are widely employed due to their simplicity and flexibility in a variety of fields. To gain more distributional information about the unknown parameters, we focus on constructing the fiducial distribution of Kriging model parameters.
Chen Fan, Shuqin Zhang, Xinmin Li
doaj +2 more sources
Lifted Brownian Kriging Models [PDF]
Technometrics, 2017 Gaussian processes have become a standard framework for modeling deterministic computer simulations and producing predictions of the response surface. This article investigates a new covariance function that is shown to offer superior prediction compared to the more common covariances for computer simulations of real physical systems.
Matthew Plumlee, Daniel W. Apley
openaire +1 more source
On the use of gradients in Kriging surrogate models [PDF]
Proceedings of the Winter Simulation Conference 2014, 2014 The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this ...
Selvakumar Ulaganathan +4 more
openaire +3 more sources
Kriging Model with Fractional Euclidean Distance Matrices [PDF]
Informatica, 2019 Summary: The multidimensional data model for kriging is developed using fractional Euclidean distance matrices (FEDM). The properties of FEDM are studied by means of the kernel matrix mehod. It has been shown that the factorization of kernel matrix enables us to create the embedded set being a nonsingular simplex.
Pozniak, Natalija +2 more
openaire +4 more sources
Mathematics, 2021 The Kriging surrogate model in complex simulation problems uses as few expensive objectives as possible to establish a global or local approximate interpolation.
Yaohui Li +5 more
doaj +1 more source
Statistical Tests for Cross-Validation of Kriging Models [PDF]
SSRN Electronic Journal, 2019 Kriging or Gaussian process models are popular metamodels (surrogate models or emulators) of simulation models; these metamodels give predictors for input combinations that are not simulated. To validate these metamodels for computationally expensive simulation models, the analysts often apply computationally efficient cross-validation. In this paper,
Jack P. C. Kleijnen, Wim C. M. van Beers
openaire +2 more sources
Nihon Kikai Gakkai ronbunshu, 2015 A Kriging-based method for efficiently searching materials was used to select additive elements in lead-free solders that are effective for suppressing the grain-boundary diffusion, which is the cause of smaller breaking elongation than lead-contained ...
Norihiko NONAKA +4 more
doaj +1 more source
IEEE Access, 2020 As the performance function of a mechanical structure is usually based on time-consuming computer codes, predicting time-dependent reliability analysis requires a large number of costly simulations in engineering.
Yutao Yan +3 more
doaj +1 more source
Grade estimation of Zu2 Jajarm deposit by considering imprecise variogram model parameters based on the extension principle [PDF]
International Journal of Mining and Geo-Engineering, 2018 Nowadays, kriging has been accepted as the most common method of grade estimation in mineral resource evaluation stage. Access to the crisp assay data and a variogram model are the necessary means for the utilization of this method. Since fitting a crisp
Saeed Soltani +2 more
doaj +1 more source
Kriging Interpolation in Modelling Tropospheric Wet Delay [PDF]
Atmosphere, 2020 This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models.
Hongyang Ma +4 more
openaire +4 more sources