Method and apparatus for analyzing a design

Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression

Reexamination Certificate

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C703S001000, C703S006000, C703S008000, C702S179000, C702S182000

Reexamination Certificate

active

06931366

ABSTRACT:
A system10,which receives a computer, aided model or design18and which probabilistically analyzes the model18by use of a modified Latin Hypercube sampling technique and combined MARS and Kriging simulation methodologies, thereby allowing a simulation to be conducted at a most probable point of operation and allowing products having desired characteristics and attributes to be created.

REFERENCES:
patent: 6377908 (2002-04-01), Ostrowski et al.
patent: 6405344 (2002-06-01), Ali et al.
Ye et al, “Algorithmic Construction of Optimal Symmetric Latin Hypercube Design,” Journal of Statistical Planning and Inference, vol. 90 No. 1, pp. 145-159 (Jul. 2000).
Bailey et al, “Using Response Surfaces to Improve the Search for Satisfactory Behavior in System Dynamics Models,” System Dynamics Review, vol. 16 No. 2, pp. 75-90 (2000).
Hamada, “Using Statistically Designed Experiments to Improve Reliability and to Achieve Robust Reliability,” IEEE Transactions on Reliability, vol. 44 No. 2, pp. 206-215 (Jun. 1995).
Boning et al, “DOE/Opt: A System for Design of Experiments, Response Surface Modeling, and Optimization Using Process and Device Simulation,” IEEE Transactions on Semiconductor Manufacturing, vol. 7 No. 2, pp. 233-244 (May 1994).
Jin, Ruichen, et al., “An Efficient Algorithm for Constructing Optimal Design of Computer Experiments” Journal of Statistical Planning and Inference, In Press, Corrected Proof, Available online Jul. 29, 2004.
William J. Welch, Robert J. Buck, Jerome Sacks, Henry P. Wynn, Toby J. Mitchell and Max D. Morris, Screening, Predicting, and Computer Experiments, Technometrics, Feb., 1992, pp. 15-25, vol. 34, No. 1, U.S.
Jerome H. Friedman, Invited Paper, Multivariate Adaptive Regression Splines, Stanford University, 1991, vol. 19, No. 1, 1-141, The Annals of Statistics, U.S.

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