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A Framework for Initial Experimental Design in the Presence of Competing Prior Knowledge

Summary: [This abstract is based on the authors' abstract.] An initial experimental design criteria is proposed for optimization problems by combining multiple, potentially competing, sources of prior information—engineering models, expert opinion, and data from past experimentation on similar, nonidentical systems. By leveraging prior information, resources used for the initial experiments are already targeted toward the optimization problem, potentially reducing the total number of resources needed in follow-up experimentation. New methodology, applicable to both computer and physical experiments, is provided for incorporating and combining conjectured models and data into both the initial modeling and experimental design stages. As a result, the experimental design criteria is flexible in how it balances space filling and objective oriented properties in the presence of conjectured prior information. An application to a thin-film growth study is provided in addition to a detailed numerical study of the design properties.

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  • Topics: Design of Experiments, Engineering
  • Keywords: Combined information, Engineering models, Bayesian model, Optimization, Process optimization, Hierarchical systems, Bayesian methods, Prior knowledge, Semiconductors, Design of experiments (DOE), Modeling
  • Author: Vastola, Justin T.; Lu, Jye-chyi; Casciato, Michael J.;Hess, Dennis W.; Grove, Martha A.;
  • Journal: Journal of Quality Technology