The origins of kriging
From MaRDI portal
Publication:5935029
DOI10.1007/BF00889887zbMath0964.86511OpenAlexW2074984119MaRDI QIDQ5935029
Publication date: 20 June 2001
Published in: Mathematical Geology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00889887
Related Items
Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process ⋮ Physics-informed machine learning with conditional Karhunen-Loève expansions ⋮ Prediction intervals for integrals of Gaussian random fields ⋮ Spatial-temporal modellization of the \(\mathrm{NO}_2\) concentration data through geostatistical tools ⋮ Bayesian hierarchical modeling: application towards production results in the Eagle Ford Shale of South Texas ⋮ 3D modeling of generalized Newtonian fluid flow with data assimilation using the least-squares finite element method ⋮ A comparison of spatial predictors when datasets could be very large ⋮ Kriging of financial term-structures ⋮ Uncertainty quantification for a sailing yacht hull, using multi-fidelity Kriging ⋮ Unnamed Item ⋮ Bayesian modelling of spatial data using Markov random fields, with application to elemental composition of forest soil ⋮ Few-shot learning for spatial regression via neural embedding-based Gaussian processes ⋮ Uncertainty Quantification of Store-Separation Simulation Due to Ejector Modeling Using a Monte Carlo Approach with Kriging Model ⋮ An expectation-maximization algorithm for the matrix normal distribution with an application in remote sensing ⋮ Approximate global optimization with convexity estimation of response surface using Kriging method ⋮ Constrained efficient global optimization with support vector machines ⋮ Inverse modelling of an aneurysm's stiffness using surrogate-based optimization and fluid-structure interaction simulations ⋮ A multiparametric strategy for the two step optimization of structural assemblies ⋮ Continuous trajectory planning of mobile sensors for informative forecasting ⋮ BUAK-AIS: efficient Bayesian updating with active learning kriging-based adaptive importance sampling ⋮ A critical assessment of Kriging model variants for high-fidelity uncertainty quantification in dynamics of composite shells ⋮ Properties and comparison of some kriging sub-model aggregation methods ⋮ Efficient spatio-temporal Gaussian regression via Kalman filtering ⋮ Generation of a cokriging metamodel using a multiparametric strategy ⋮ Statistical Implementations of Agent‐Based Demographic Models ⋮ Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions ⋮ Multi-fidelity surrogate-based optimal design of road vehicle suspension systems ⋮ Informative windowed forecasting of continuous-time linear systems for mutual information-based sensor planning ⋮ Structural optimization using kriging approximation ⋮ Unnamed Item ⋮ A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models ⋮ Just interpolate: kernel ``ridgeless regression can generalize ⋮ Variogram calculations for random fields on regular lattices using quadrature methods ⋮ DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction ⋮ Spatial Regression Models Using Inter-Region Distances in a Non-Random Context ⋮ A unified framework for stochastic optimization ⋮ High-dimensional intrinsic interpolation using Gaussian process regression and diffusion maps ⋮ An efficient algorithm for kriging approximation and optimization with large-scale sampling data. ⋮ Including a Nugget Effect in Lifted Brownian Covariance Models ⋮ On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust ⋮ High-fidelity hurricane surge forecasting using emulation and sequential experiments ⋮ Uncertainty quantification in multiscale simulation of woven fiber composites ⋮ System development and application of Taylor Kriging metamodeling ⋮ Systems of Gaussian process models for directed chains of solvers ⋮ On the equivalence of kriging and maximum entropy estimators ⋮ Gradient-based methods for uncertainty quantification in hypersonic flows ⋮ Spatial modeling of trends in crime over time in Philadelphia ⋮ Lithology prediction in the subsurface by artificial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method ⋮ Fixed Rank Kriging for Very Large Spatial Data Sets ⋮ Filtered Kriging for Spatial Data with Heterogeneous Measurement Error Variances ⋮ Mapping interstellar dust with Gaussian processes ⋮ Comparing and selecting spatial predictors using local criteria ⋮ Low-rank multi-parametric covariance identification
Cites Work