When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensiv...

Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable sel...

We present a comparative study of soil CO2 flux (FCO2) measured by five groups (Groups 1-5) at the IAVCEI-CCVG Eighth Workshop on Volcanic Gases on Masaya volcano, Nicaragua. Groups 1-5 measured FCO2 using the accumulation chamber method at 5-m spacing within a 900 m grid during a morning (AM) period. These measurements were repeated by Groups 1-3 during an afternoon (PM) period. All measured F...

[1] During the last decade one has witnessed an increasing interest in assessing health risks caused by exposure to contaminants present in the soil, air, and water. A key component of any exposure study is a reliable model for the space-time distribution of pollutants. This paper compares the performances of multi-Gaussian and indicator kriging for modeling probabilistically the spatial distri...

The most productive way to increase the availability of single frequency users of the Wide Area Augmentation System (WAAS) is by decreasing the Grid Ionospheric Vertical Error (GIVE). Currently the GIVE’s are very conservative, since WAAS has to protect against the worst possible case of ionospheric behavior given the measurements. By characterizing more accurately the vertical ionospheric dela...

Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms different fidelity. The auto-regressive co-kriging model provides suitable framework analysis such data sets as it is able account cross-dependencies among fidelity outputs. However, its implem...

1. Abstract Over three decades, metamodeling has been widely applied to design optimization problems to build a surrogate model of computation-intensive engineering models. The Kriging method has gained significant interests for developing the surrogate model. However, traditional Kriging methods, including the ordinary Kriging and the universal Kriging, use fixed polynomials basis functions to...

The problem of aircraft engine condition monitoring based on vibration signals is addressed. To do so, we compare two estimators of the Frequency Response Function of an aircrat engine which input is its shaft angular position and which output is an accelerometric signal that measures vibrations. It is shown that this problem can be seen as a smoothing problem, and that linear kernel smoothing ...

Mapping of groundwater level observations often makes very little use of auxiliary data and is often undertaken simply by manual interpolation or ordinary kriging of the heads. Recently, a number of geostatistical methods have emerged that significantly improve estimates by incorporating the land surface elevation and groundwater flow or drawdown equations. However, at the regional scale heads ...

The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimizat...