Results 21 to 30 of about 13,817,849 (263)
Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach [PDF]
International Journal of Data and Network Science, 2023 Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model.
Annisa Nur Falah +3 more
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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
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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
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Scalarizing cost-effective multiobjective optimization algorithms made possible with kriging
, 2007 The use of kriging in cost-effective single-objective optimization is well established, and a wide variety of different criteria now exist for selecting design vectors to evaluate in the search for the global minimum.
Jan Sykulski +4 more
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Pca & Kriging For Surrogate Models
2nd General Meeting and 2nd Workshop on Smart Energy Carriers in Industry, 2016 Presentation at the 2nd General Meeting and 2nd Workshop on Smart Energy Carriers in ...
Aversano, Gianmarco +2 more
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Kriging modelization in predicting metal sheet elongation [PDF]
Quality and Reliability Engineering International, 2018 AbstractModern industries are increasingly replacing real experiments with non‐stochastic simulation models due to their restrained costs and growing reliability. The non‐stochastic simulator used in this paper is the Finite Element simulation code (FEM), a widely used numerical technique for the engineering problems modeled by a system of partial ...
Vicario G., Giraudo M. T., Cali V.
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Applied Sciences, 2019 Dynamic model updating based on finite element method (FEM) has been widely investigated for structural damage identification, especially for static structures. Despite the substantial advances in this method, the key issue still needs to be addressed to
Zhiwen Lu, Yong Lv, Huajiang Ouyang
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Assessment of Groundwater Quality Spatial Distribution and Appointment Optimize Network of Shahrkord Plain Aquifer Using Geostatistical Methods [PDF]
پژوهشهای آبخیزداری, 2019 As the most valuable natural resources, protection and management of groundwater is vital. One of the best ways to protect groundwater resources is monitoring groundwater quality spatial variationsand then managing the water resources exploitations and ...
Mehdi Abdollahi Mansourkhani +3 more
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Small ensembles of kriging models for optimization
CoRR, 2016 The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP.
Hossein Mohammadi +2 more
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Kriging Models that are Robust with Respect to Simulation Errors [PDF]
SSRN Electronic Journal, 2007 In the field of the Design and Analysis of Computer Experiments (DACE) meta-models are used to approximate time-consuming simulations. These simulations often contain simulation-model errors in the output variables. In the construction of meta-models, these errors are often ignored. Simulation-model errors may be magnified by the meta-model. Therefore,
Siem, A.Y.D., Hertog, D. den
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