Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-22T19:10:59.830Z Has data issue: false hasContentIssue false

Learning abstract models for system design

Published online by Cambridge University Press:  27 February 2009

Sudhakar Y. Reddy
Affiliation:
Rockwell International Science Center, Palo Alto Laboratory, 444 High Street, Suite 400, Palo Alto, CA 94301, U.S.A.

Abstract

Though simulation models are extensively used for detailed design analysis, they find limited role in preliminary design decisions. We have developed a machine learning based approach to enable detailed simulation models to be harvested for supporting early-stage design of engineering systems.

Type
Research Abstracts
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Reddy, S.Y. (1994). Hierarchical and interactive parameter refinement for early stage system design. Ph.D. thesis, University of Illinois at Urbana-Champaign, Department of Mechanical Engineering, Urbana, IL.Google Scholar
Reddy, S.Y., & Lu, S.C.-Y. (1994). An interactive refinement methodology for early stage exploration of design space during system design. Annals of the CIRP 43(1).CrossRefGoogle Scholar
Tcheng, D., Lambert, B., Lu, S.C.-Y., & Rendell, L. (1989). Building robust learning systems by combining induction and optimizations. Proc. Eleventh IJCAI, 308314.Google Scholar
Yerramareddy, S., & Lu, S.C.-Y. (1993). Hierarchical and interactive decision refinement methodology for engineering design. Res. Eng. Design 4(4), 227239.CrossRefGoogle Scholar
Yerramareddy, S., Tcheng, D.T., Lu, S.C.-Y., & Assanis, D.N. (1992). Creating and using models for engineering design. IEEE Expert 7(3), 5259.CrossRefGoogle Scholar