Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1996 · 200 citations · 30 references
EngineeringElectrical EngineeringSystems EngineeringCircuit TheoryDesign ComputationSimulation-based DesignComplex Systems ModelingComputational ModelingDesign SimulationSimulation ModelingSystem DecompositionSystem SimulationCircuit SimulationModel Order ReductionSparse Linear AlgebraSparse Matrix AlgorithmsSparse Matrix ComputationSparse Matrix Techniques
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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Design and Analysis of Computer Experiments
Jerome Sacks, William J. Welch, Toby J. Mitchell et al. · Statistical Science · 1989 · 7K citations · Full text
Large Sample Properties of Simulations Using Latin Hypercube Sampling
Michael L. Stein · Technometrics · 1987 · 2K citations