Abstract. Spatial Gaussian Processes, alias spatial linear models or Kriging estimators, are a powerful and wellestablished tool for the design and analysis of computer experiments in a multitude of engineering applications. A key challenge in constructing spatial Gaussian processes is the training of the predictor by numerically optimizing its associated maximum likelihood function depending o...

Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algor...

Introduction: The present study aimed to compare the performance of two widely-used models for spatial assessment of particulate matter less than 10 microns (PM10) in ambient air of Yazd city. Finally, effective factors on concentrations of pollutants and corresponding standards were investigated. Materials and Methods: A number of 13 sampling stations were employed in different areas of Yazd ...

A study was designed to compare the performance of six spatial interpolation methods to estimate soil properties at unvisited points. These methods were global mean, moving average, nearest neighbour, inverse squared distance, Laplacian smoothing splines and ordinary point kriging. These methods were also applied in combination with a choropleth map (soil map) by stratifying the area. The soil ...

This paper proposes a novel method to select an experimental design for interpolation in random simulation. (Though the paper focuses on Kriging, this method may also apply to other types of metamodels such as linear regression models.) Assuming that simulation requires much computer time, it is important to select a design with a small number of observations (or simulation runs). The proposed ...

In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of the Artificial Neural Networks (ANNs) technique in ore grade estimation. Different training algorithms and numbers of hidden neurons are applied to estimate Cu grade of borehole data in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province (Iran). The ef...

The sustainable management of groundwater resources needs quantitative information on its behaviors in space and time. With the availability of distributed models, which can handle large volume of data, the spatial information of groundwater at a grid pattern is not only useful but is necessary to get reliable results. Various methods, both simple and complicated, are available and are in use t...