Bayesian-validated computer-simulation surrogates for optimization and design: Error estimates and applications

John Otto, Marius Paraschivoiu, Serhat Yesilyurt and Anthony T. Patera

Mathematics and Computers in Simulation (MATCOM), 1997, vol. 44, issue 4, 347-367

Abstract: We present a Bayesian-validated surrogate framework which permits economical and reliable integration of large-scale simulations into engineering design and optimization. In the surrogate approach, the large-scale simulation is evoked only to construct and validate a simplified input-output model; this simplified input-output model then serves as a simulation surrogate in subsequent engineering optimization studies. The distinguishing features of our approach are: sequential statistical sampling procedures which permit both efficient adaptive surrogate construction and rigorous ‘probably-approximately-correct’ surrogate validation; and validation-based non-parametric a posteriori error estimates which precisely quantify the effect of surrogate-for-simulation substitution on system predictability and optimality. In this paper we discuss recent improvements and extensions to our construction-validation algorithms and a posteriori error framework, and present several illustrative applications in heat transfer and fluid mechanics.

Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:44:y:1997:i:4:p:347-367

DOI: 10.1016/S0378-4754(97)00061-X

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