Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential design and rational metamodelling
WSC '05 Proceedings of the 37th conference on Winter simulation
Stochastic gradient estimation using a single design point
Proceedings of the 38th conference on Winter simulation
The Journal of Machine Learning Research
Hierarchical Nonlinear Approximation for Experimental Design and Statistical Data Fitting
SIAM Journal on Scientific Computing
Multidimensional sequential sampling for NURBs-based metamodel development
Engineering with Computers
Towards a Black Box Algorithm for Nonlinear Function Approximation over High-Dimensional Domains
SIAM Journal on Scientific Computing
Multidimensional adaptive sampling and reconstruction for ray tracing
ACM SIGGRAPH 2008 papers
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Adaptive distributed metamodeling
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
Evolutionary regression modeling with active learning: an application to rainfall runoff modeling
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
The Journal of Machine Learning Research
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
ASK: adaptive sampling kit for performance characterization
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
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Many complex real-world systems can be accurately modeled by simulations. However, high-fidelity simulations may take hours or even days to compute. Because this can be impractical, a surrogate model is often used to approximate the dynamic behavior of the original simulator. This model can then be used as a cheap, drop-in replacement for the simulator. Because simulations can be very expensive, the data points, which are required to build the model, must be chosen as optimally as possible. Sequential design strategies offer a huge advantage over one-shot experimental designs because they can use information gathered from previous data points in order to determine the location of new data points. Each sequential design strategy must perform a trade-off between exploration and exploitation, where the former involves selecting data points in unexplored regions of the design space, while the latter suggests adding data points in regions which were previously identified to be interesting (for example, highly nonlinear regions). In this paper, a novel hybrid sequential design strategy is proposed which uses a Monte Carlo-based approximation of a Voronoi tessellation for exploration and local linear approximations of the simulator for exploitation. The advantage of this method over other sequential design methods is that it is independent of the model type, and can therefore be used in heterogeneous modeling environments, where multiple model types are used at the same time. The new method is demonstrated on a number of test problems, showing that it is a robust, competitive, and efficient sequential design strategy.