Vol.1, No.1, 2000, pp.127-140, doi:10.3970/cmes.2000.001.127
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ARTICLE
Optimal Design of Computer Experiments for Metamodel Generation Using I-OPTTM
  • Selden B. Crary1, Peter Cousseau2, David Armstrong1, David M. Woodcock3, Eva H. Mok1, Olivier Dubochet4, Philippe Lerch4, Philippe Renaud2
Ctr. for Integrated MicroSystems, U. Mich., Ann Arbor, MI, USA
Institute for Microsystems, EPFL, Ecublens, CH
Lab. for Scientific Computation, U. Mich., Ann Arbor, MI, USA
Leister Process Technologies, Sarnen, CH
Abstract
We present a new and unique software capability for finding statistical optimal designs of deterministic experiments on continuous cuboidal regions. The objective function for the design optimization is the minimization of the expected integrated mean squared error of prediction of the metamodel that will be found, subsequent to the running of the computer simulations, using the best linear unbiased predictor (BLUP). The assumed response-model function includes an unknown, stochastic term, Z. We prove that this criterion, which we name IZ-optimality, is equivalent to I-optimality for non-deterministic experiments, in the limit of zero correlations among the Z's for different inputs. An example is presented of metamodel generation for a micromachined-silicon flow sensor. The IZ-optimal set of inputs is found, finite-element (FE) simulations run, and the metamodel generated using a BLUP fit. The method is compared to other approaches. IZ-optimality, coupled with BLUP fitting, provides a highly efficient means of non-parametric metamodel generation. IZ-optimal design searching and BLUP fitting are new options of the I-OPTTM program that is available on the World-Wide Web at URL http://www-personal.engin.umich.edu/~crary/iopt.
Keywords
design of computer experiments, I-optimality, microelectromechanical systems, MEMS, silicon flow sensor
Cite This Article
Crary, S. B., Cousseau, P., Armstrong, D., Woodcock, D. M., Mok, E. H. et al. (2000). Optimal Design of Computer Experiments for Metamodel Generation Using I-OPTTM. CMES-Computer Modeling in Engineering & Sciences, 1(1), 127–140.
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