Article1996

Experimental Design and Observation for Large Systems

Ron Bates · Robert J. Buck · Eva Riccomagno · Henry P. Wynn

200 citations0/0 files storedJournal of the Royal Statistical Society Series B (Statistical Methodology)

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Optimal Experimental Design Methods Simulation Techniques and Applications Probabilistic and Robust Engineering Design

Abstract

SUMMARY Large systems require new methods of experimental designs suitable for the highly adaptive models which are employed to cope with complex non-linear responses and high dimensionality of input spaces. The area of computer experiments has started to provide such designs especially Latin hypercube and lattice designs. System decomposition, prevalent in several branches of engineering, can be employed to decrease complexity. A combination of system decomposition using a sparse matrix method, experimental design and modelling is applied to one example of an electrical circuit simulator producing a usable emulator of the circuit for use in optimization and sensitivity analysis.

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References

1989Design and Analysis of Computer Experiments 1987Large Sample Properties of Simulations Using Latin Hypercube Sampling 1990Minimax and maximin distance designs 1989Designs for Computer Experiments 1959Optimum Designs in Regression Problems 1992Screening, Predicting, and Computer Experiments 1987Maximum entropy sampling 1993Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction

Cited by

2010Space-filling Latin hypercube designs for computer experiments 1999Coherent dispersion criteria for optimal experimental design 2002Uniform designs limit aliasing 2003Ch. 4. Uniform experimental designs and their applications in industry 2000Miscellanea. A connection between uniformity and aberration in regular fractions of two-level factorials 2003Optimal mixed-level supersaturated design 2008Sequential Optimal Design of Neurophysiology Experiments

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