Proceedings of the 30th conference on Winter simulation
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
Model abstraction for discrete event systems using neural networks and sensitivity information
Proceedings of the 32nd conference on Winter simulation
Principles of Digital Image Synthesis
Principles of Digital Image Synthesis
Kriging metamodeling in discrete-event simulation: an overview
WSC '05 Proceedings of the 37th conference on Winter simulation
Sequential design and rational metamodelling
WSC '05 Proceedings of the 37th 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
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
Data driven design optimization methodology a dynamic data driven application system
ICCS'03 Proceedings of the 2003 international conference on Computational science
Implementation of a grid-enabled problem solving environment in matlab
ICCS'03 Proceedings of the 2003 international conference on Computational science
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
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments
SIAM Journal on Scientific Computing
An alternative approach to avoid overfitting for surrogate models
Proceedings of the Winter Simulation Conference
Enhanced metamodeling techniques for high-dimensional IC design estimation problems
Proceedings of the Conference on Design, Automation and Test in Europe
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In mathematical/statistical modeling of complex systems, the locations of the data points are essential to the success of the algorithm. Sequential design methods are iterative algorithms that use data acquired from previous iterations to guide future sample selection. They are often used to improve an initial design such as a Latin hypercube or a simple grid, in order to focus on highly dynamic parts of the design space. In this paper, a comparison is made between different sequential design methods for global surrogate modeling on a real-world electronics problem. Existing exploitation and exploration-based methods are compared against a novel hybrid technique which incorporates both an exploitation criterion, using local linear approximations of the objective function, and an exploration criterion, using a Monte Carlo Voronoi tessellation. The test results indicate that a considerable improvement of the average model accuracy can be achieved by using this new approach.