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Fri., May 21, 2010, 11 a.m. Osborne Conference Room
(ECSS 3.503)







 me lecture

“Metamodeling Based Design Optimization”
Dr. Rahul Rai, California State University, Fresno

This presentation will highlight some new results in the area of metamodeling and sequential sampling. Mathematical models have been widely used to simulate and analyze complex real-world systems in the area of engineering design. These mathematical models, often implemented by computer codes (e.g., Computational Fluid Dynamics and Finite Element Analysis), could be computationally expensive. A widely used strategy is to use approximation models, which are often referred to as metamodels as they provide a model of the model, replacing the expensive simulation model during the process. An important research issue related to metamodeling is how to achieve a good accuracy of a metamodel with a reasonable number of sample points. While the accuracy of a metamodel is directly related to the metamodeling technique used and the properties of a problem itself, the types of sampling approaches also have direct influences on the performance of a metamodel. The presentation outlines a qualitative and quantitative sequential sampling (Q2S2) technique in which optimization and user knowledge guide the efficient choice of sample points. This method combines information from multiple fidelity sources, including computer simulation models of the product, first principals involved in design and the designer’s qualitative intuitions about the design. Both quantitative and qualitative information from different sources are merged together to arrive at a new sampling strategy. The seminar will also outline other ongoing/future research projects, including click and create CAD system (multi-stereo algorithms based CAD models) and Product Service System based system design to mitigate e-waste and the Osborne effect.

Rahul Rai is an assistant professor of mechanical engineering at Fresno State. His primary research areas are design automation and optimization, adaptive design of experiments (DOE), metamodeling, empirical similitude, design theory for managing product obsolescence, ontology-based decision support and knowledge representation of design problems, and computational design synthesis using graph grammar. He earned his PhD in mechanical engineering from UT Austin in 2006 and was then a postdoc in the NSF center for e-Design at Virginia Tech. For more information visit