Automatically assigned DDC number: 00631

Manually assigned DDC number: 00631

Number of references: 33

Title: Artificial Intelligence Technologies in Complex Engineering Design

Author:

Subject: Yew Soon Ong Artificial Intelligence Technologies in Complex Engineering Design

Description: COMPUTATIONAL ENGINEERING AND DESIGN CENTER SCHOOL OF ENGINEERING SCIENCES Doctor of Philosophy Artificial Intelligence Technologies in Complex Engineering Design by Yew Soon Ong Engineering design optimization is an emerging technology whose application both tends to shorten design-cycle time and finds new designs that are not only feasible, but also nearer to optimum, based on specified design criteria. Its gain in attention in the field of complex designs is fuelled by advancing computing power now allowing increasingly accurate analysis codes to be deployed. Unfortunately, the optimization of complex engineering design problems remains a di#cult task, due to the complexity of the cost surfaces and the human expertise necessary in order to achieve high quality results. This research is concerned with the e#ective use of past experiences and chronicled data from previous designs to mitigate some of the limitations of present engineering design optimization process. In particular, the present work leverages well established artificial intelligence technologies and extends recent theoretical and empirical advances, particularly in machine learning, adaptive hybrid evolutionary computation, surrogate modeling, radial basis functions and transductive inference, to mitigate the issues of i) choice of optimization methods and ii) dealing with expensive design problems. The resulting approaches are studied using commonly employed benchmark functions. Further demonstrations on realistic aerodynamic aircraft and ship design problems reveal that the proposed techniques not only generate robust design performance, they can also greatly decrease the cost of design space search and arrive at better designs as compared to conventional approaches.

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2004-05-09

Pubyear: 2002

Format: pdf

Identifier: http://citeseer.ist.psu.edu/632871.html

Source: http://sentosa.sas.ntu.edu.sg:8000/~asysong/thesis.pdf

Language: en

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