A Polynomial Induction Network Approach to Engineering Design

SUMMARY

A key aspect of design considers the effects of large groups of design variables on multiple measures of system performance or responses. Thus, a goal of design-aiding systems is to learn the contributions of each design variable to the response variables. This problem is particularly difficult when simulation runs are expensive in either time or money because conducting exhaustive searches over the design space is not possible. This situation occurs quite frequently for large-scale design problems where the number of design variables is large and their relationships to the response variables are not well understood. In this paper, we present a polynomial induction network (PIN) methodology that will support a designer in problems of this sort, where design variables are numerous and simulations are expensive. Our description shows how this approach was implemented in software and used to actually design a multi-processor system. Based on this experience, we conclude that PINs have excellent promise for supporting engineering design activities.