COMPUTER EXPERIMENTS: PROMISING NEW FRONTIERS IN ANALYSIS AND DESIGN OF EXPERIMENTS 1

In the last 20 years replacing entirely or partially the physical experiments with the numerical ones has become increasingly popular and it is a daily practice. A genuine reason for a single or a combined approach is that both the physical experimentation is sometimes unapproachable or extremely expensive, and the use of the codes in the product/ process development phase have become straightforward and quite inexpensive. The latter may represent also very complex systems and may reduce the deal at both design and analysis stages, cutting down to the minimum the preparation of very expensive prototypes. The general availability of comprehensive computing facilities and the recent progresses in software development make numerical simulation of complex systems an attractive alternative option to the execution of the expensive and time consuming physical experiments. In this paper, different techniques for modeling and design a simulated experiment are exhibited according to the more recent literature. A separately paragraph will be devoted to an exhibition of a few case studies for which the physical experiment had been replaced with a simulated one.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  I. Sobol.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[3]  Anthos Bray,et al.  The role of stress analysis in the design of force-standard transducers , 1981 .

[4]  I. Sobol.  Global Sensitivity Indices for Nonlinear Mathematical Models , 2004 .

[5]  Eva Riccomagno,et al.  Experimental Design and Observation for Large Systems , 1996, Journal of the Royal Statistical Society: Series B (Methodological).

[6]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[7]  Martin T. Hagan,et al.  Neural network design , 1995 .

[8]  David Francis Walnut,et al.  Wavelets: A Tutorial in Theory and Applications (Charles K. Chui, ed.) , 1993, SIAM Rev..

[9]  Trevor Hastie,et al.  Polynomial splines and their tensor products in extended linear modeling. Discussion and rejoinder , 1997 .

[10]  Grazia Vicario,et al.  Robust FEM experiments , 2000 .

[11]  Michael Frenklach,et al.  SENSITIVITY ANALYSIS AND PARAMETER ESTIMATION IN DYNAMIC MODELING OF CHEMICAL KINETICS , 1983 .

[12]  Raffaello Levi,et al.  Simultaneous Parameter and Tolerance Design by Sequential Numerical Experiments , 1999 .

[13]  Singiresu S. Rao.  The finite element method in engineering , 1982 .

[14]  Jian An,et al.  Quasi-regression , 2001, J. Complex..

[15]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[16]  Jerome Sacks,et al.  Designs for Computer Experiments , 1989 .

[17]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[18]  Grazia Vicario,et al.  Reliable Estimation in Computer Experiments on Finite-Element Codes , 2002 .

[19]  C. Chui.  Wavelets: A Tutorial in Theory and Applications , 1992 .

[20]  B. Avitzur.  Metal forming: Processes and analysis , 1979 .

[21]  G. Wahba.  Spline models for observational data , 1990 .

[22]  Grazia Vicario,et al.  Multiresponse Robust Design: A General Framework Based on Combined Array , 2004 .

[23]  O. Zienkiewicz.  The Finite Element Method In Engineering Science , 1971 .

[24]  A. Cohen.  Ten Lectures on Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 61, I. Daubechies, SIAM, 1992, xix + 357 pp. , 1994 .

[25]  J. Kleijnen.  Statistical tools for simulation practitioners , 1986 .

[26]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[27]  Jianqing Fan.  Design-adaptive Nonparametric Regression , 1992 .

[28]  H. Wynn,et al.  Lattice-based D-optimum design for Fourier regression , 1997 .

[29]  Daniele Romano,et al.  Design for robustness and cost effectiveness: the case of an optical profilometer , 2005 .

[30]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[31]  H. Müller,et al.  Local Polynomial Modeling and Its Applications , 1998 .

[32]  T. J. Mitchell,et al.  Exploratory designs for computational experiments , 1995 .

[33]  Wai Man Ho.  Case studies in computer experiments, applications of uniform design and modern modeling techniques , 2001 .