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Sequential Experiment Design for Contour Estimation From Complex Computer Codes
TLDR
We develop a sequential design methodology for estimating a contour (also called a level set or iso-surface) of a complex computer code. Expand
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Modeling an Augmented Lagrangian for Blackbox Constrained Optimization
TLDR
We propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework to address the problem of constrained blackbox optimization . Expand
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A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data
TLDR
We propose a lower bound on the nugget that minimizes the over-smoothing and an iterative regularization approach to construct a predictor that further improves the interpolation accuracy. Expand
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GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs
TLDR
This paper presents a new R package GPfit for robust and computationally efficient fitting of GP models to deterministic computer simulators. Expand
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Sequential design for computer experiments with a flexible Bayesian additive model
TLDR
In computer experiments, a mathematical model implemented on a computer is used to represent complex physical phenomena. Expand
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Modeling an augmented Lagrangian for improved blackbox constrained optimization
TLDR
We propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework for constrained blackbox optimization. Expand
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A new Bayesian ensemble of trees approach for land cover classification of satellite imagery
Classification of satellite images is a key component of many remote sensing applications. One of the most important products of a raw satellite image is the classification that labels image pixelsExpand
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Follow-up Experimental Designs for Computer Models and Physical Processes
TLDR
We develop a methodology for selecting optimal follow-up designs based on integrated mean squared error that help us capture and reduce prediction uncertainty as much as possible. Expand
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A Short Note on Gaussian Process Modeling for Large Datasets using Graphics Processing Units
TLDR
We demonstrate that the computational cost of implementing GP models can be significantly reduced by using a CPU+GPU heterogeneous computing system over an analogous implementation on a traditional computing system with no GPU acceleration. Expand
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Branch and Bound Algorithms for Maximizing Expected Improvement Functions
TLDR
We develop branch and bound algorithms for efficiently maximizing the EI function in specific problems, including the simultaneous estimation of global maximum and minimum, and in the estimation of a contour. Expand
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