Article

Free access

Metamodels for simulation input-output relations

Author:

Russell R. BartonAuthors Info & Claims

WSC '92: Proceedings of the 24th conference on Winter simulation

Pages 289 - 299

https://doi.org/10.1145/167293.167352

Published: 01 December 1992 Publication History

PDF eReader

References

[1]

Anderson, K.R. 1987. A method for planning analysis and design simulation of CIM systems. Proceedings of the 1987 Winter Simulation Conference, A. Thesen, H. Grant, W. D. Kelton eds. 715-720.

Digital Library

Google Scholar

[2]

Avramidis, T.N. and Wilson, J.R. 1990. Control variates for stochastic network simulation. Proceedings of the 1990 Winter Simulation Conference, O. Balci, R.P. Sadowski, and R.E. Nanoz eAs. 323-332.

Digital Library

Google Scholar

[3]

Biles, W.E. 1974. A gradient-regression search procedure for simulation experimentation. Proceedings of the 1974 Winter Simulation Conference, H.J. Highland ed. 490-497.

Digital Library

Google Scholar

[4]

Box, G.E.P. 1954. The exploration and exploitation of response surfaces: some general considerations and examples. Biometrics, 10, 16-60.

Crossref

Google Scholar

[5]

Box, G.E.P. and Draper, N.R. 1987. Empirical Model- Building and Response Surfaces. New York: John Wiley and Sons.

Digital Library

Google Scholar

[6]

Box, G.E.P., Hunter, W.G., and Hunter, J.S. 1978. Statistics for Experimenters. New York: John Wiley and Sons.

Google Scholar

[7]

Box, G.E.P. and Youle, P.V. 1955. The exploration and exploitation of response surfaces: an example of the link between the fitted surface and the basic mechanism of the system. Biometrics, 11,287-323.

Crossref

Google Scholar

[8]

Breiman, L. 1991. The H method for estimating multivariate functions from noisy data. Technometrics, 33, 125-160 (with discussion).

Digital Library

Google Scholar

[9]

Buss, A.H. 1990. System Identification Using Frequency Domain Methodology. Proceedings of the 1990 Winter Simulation Conference, O. Balci, R.P. Sadowski, and R.E. Nance eds. 360-363.

Digital Library

Google Scholar

[10]

Cheng, R.H. 1990. Fitting parametric models by conditional simulation. Proceedings of the 1990 Winter Simulation Conference, O. Balci, R.P. Sadowski, and R.E. Nanc.~ eds. 333-336.

Digital Library

Google Scholar

[11]

Craven, P. and Wahba, G. 1979. Smoothing noisy data with spline functions. Numerische Mathematik, 31,377-403.

Digital Library

Google Scholar

[12]

Currin, C., Mitchell, T.J., Morris, M., and Ylvisaker, D. 1991. Bayesian prediction of deterministic functions, with application to the design and analysis of computer experiments. Journal of the American Statistical Association, 86, 953-963.

Crossref

Google Scholar

[13]

Daubeehies, i. 1988. Orthonormal bases of compactly supported wavelets. Communications in Pure and Applied Mathematics, 41,909-996.

Crossref

Google Scholar

[14]

deBoor, C. 1978. A Practical Guide to Splines. New York: Springer-Verlag.

Google Scholar

[15]

Donohue, J.M., Houek, E.M., and Myers, R.H. 1990. Some optimal simulation designs for estimating quadratic response surface functions. Proceedings of the 1990 Winter Simulation Conference, O. Balci, R.P. $adowski, and R.E. Nanee eds. 337-343.

Digital Library

Google Scholar

[16]

Draper, N.R, artd Guttman, I. 1986. Response surface designs in flexible regions. Journal of the American Statistical Association, 8 I, 1089-1094.

Crossref

Google Scholar

[17]

Dyn, N. 1987. Interpolation of scattered data by radial functions. Topics in Multivariate Approximation, C.K. Chui and L.L. Schumaker eds. Boston: Academic Press.

Google Scholar

[18]

Dyn, N., Levin, D., and Rippa, S. 1986, Numerical procedures for surface fitting of scattered data by radial functions SlAM Journal of Scientific and Statistical Computing, 7, 639-659.

Google Scholar

[19]

Eubank, R.L. 1988. Spline Smoothing and Nonparametrie Regression. New York: Marcel Dekker.

Google Scholar

[20]

Friedman, J.H. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19, 1-141.

Crossref

Google Scholar

[21]

Glynn, P.W. 1987. Likelihood ratio gradient estimation: an overview. Proceedings of the 1987 Winter Simulation Conference A. Thesen, H. Grant, W.D. Kelton eds. 366- 375.

Digital Library

Google Scholar

[22]

Grosse, E.H. 1980. Tensor spline approximation. L/near Algebra and Its Applications, 34, 29-41.

Google Scholar

[23]

Gu, C. 1990. Adaptive spline smoothing in non-gaussian regression models. Journal of the American Statisticai Association, 85, 801-807.

Google Scholar

[24]

Gu, C., Bates, D.M., Chert, Z., and Wahba, G. 1989. The computation of GCV functions through householde, tridiagonalization with application to the fitting ot interaction spline models. SlAM Journal on Matrix Analysis and Applications, 10, 457-480.

Digital Library

Google Scholar

[25]

H~dle, W. 1990. Applied Nonparametric Regression. Cambridge: Cambridge University Press.

Google Scholar

[26]

H~dle, W. and Matron, J.S. 1985. Optimal bandwidth selection in nonparametric regression function estimation. The Annals of Statistics, 13, 1465-1481.

Crossref

Google Scholar

[27]

Ho, Y.C. and Li, S. 1988. Extensions of infinitesimal perturbation analysis. IEEE Transactions on Automatic Control, AC-33, 427-438.

Crossref

Google Scholar

[28]

Hunter, J.S. 1958-59. Determination of optimal operating conditions by experimental methods. Industrial Quality Control, 15, Part II-1 (Dec. 1958), Part II-2 (Jan. 1959), and 16, Part 11-3 (Feb. 1959).

Google Scholar

[29]

Jacobson, S., Buss, A.H., and Schruben, L.W. 1992. Driving frequency selection for frequency domain experiments. Operations Research(to appear).

Digital Library

Google Scholar

[30]

Kacker, R.N. and Tsui, K. 1990. Interaction graphs: graphical aids for planning experiments. Journal of Quality Technology, 22, 1-14.

Crossref

Google Scholar

[31]

Khuri, A.I. and Cornell, J.A. 1987. Response Surfaces, Designs, and Analyses. New York: M. Dekker.

Digital Library

Google Scholar

[32]

Kleijnen, J.P.C. 1987. Statistical Tools for Simulation Practitioners. New York: Marcel Dekker.

Digital Library

Google Scholar

[33]

Lewis, P.A.W. and Stevens, J.G. 1991. Nonlinear modeling of time series using multivariate adaptive regression splines (MARS). Journal of the American Statistical Association, 86, 864-877.

Crossref

Google Scholar

[34]

Mason, R.L. Gunst, R.F., and He~s. J.L. 1989. Statistical Design and Analysis of Experiments with Applications to Engineering and Science. New York: John Wiley and Sons.

Google Scholar

[35]

McCullagh, P. and Nelder, J.A. 1989. Generalized Linear Models. Second Edition, London: Chapman and Hall.

Google Scholar

[36]

Mihram, G.A. 1970. An efficient procedure for locating the optimal simular response. Proceedings of the Fourth Conference on the Applications of Simulation, New York: Association for Computing Machinery.

Digital Library

Google Scholar

[37]

Montgomery, D.C. 1991. Using Fractional factorial designs for robust design process development. Quality Engineering, 3, 193-205.

Crossref

Google Scholar

[38]

Mortice, D.J. 1991. The effects of batching on the power of the test for frequency domain methodology. Proceedings of the 1991 Winter Simulation Conference, B.L. Nelson, W. D. Kelton, G.M. Clark eds. 1018-1022.

Digital Library

Google Scholar

[39]

Myers, R.H. 1976. Response Surface Methodology. Boston: Allyn and Bacon.

Google Scholar

[40]

Myers, R.H., Khuri, A.I., and Carter, W.H. Jr. 1989. Response surface methodology: 1966-1988. Technometrics, 31, 137- 157.

Digital Library

Google Scholar

[41]

Nair, V.N. et. al. 1992. Taguchi's parameter design: a panel discussion. Technometrics, 34, 127-161.

Digital Library

Google Scholar

[42]

Nelder, J.A. and Lee, Y. 1991. Generalized linear models for the analysis of Taguchi-type experiments. Applied Stochastic Models and Data Analysis, 7, 107-120.

Crossref

Google Scholar

[43]

Nozari, A., Arnold, S.F., and Pegden, C.D. 1987. Statistical analysis for use with the Schruben and Margolin correlation induction strategy. Operations Research, 35, 127-139.

Digital Library

Google Scholar

[44]

Payne, C.D. ed. 1986. The GLIM Manual, release 3.77. Oxford: Numerical Algorithms Group (NAG).

Google Scholar

[45]

Phadke, M. 1989. Quality Engineering Using Robust Design. Englewood Cliffs, NJ: Prentice-Hall.

Digital Library

Google Scholar

[46]

Porta Nora, A. M. and Wilson, J.R. 1989. Estimating multiresponse simulation metamodels using control variates. Management Science, 35, 1316-1333.

Digital Library

Google Scholar

[47]

Racite, M.P. and Lawlor, F.D. 1972. Techniques for the development of empirical subsystem models. Proceedings of the 1972 Summer Simulation Conference, M.K. Horn ed. 155-162.

Google Scholar

[48]

Ramberg, J.S., Sanchez, S.M., Sanchez, P.J., and Hollick, L.J. 1991. Designing simulation experiments: Taguchi methods and response surface metamodels. Proceedings of the 1991 Winter Simulation Conference, B.L. Nelson, W. D. Kelton, G.M. Clark eds. 167-176.

Digital Library

Google Scholar

[49]

Rice, J. 1984. Boundary modification for kernel regression. Communications in Statistics - Theory and Methodology (Part A), 13, 893-900.

Crossref

Google Scholar

[50]

Rubinstein, R.Y. 1986. Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks. New York: John Wiley and Sons.

Digital Library

Google Scholar

[51]

Runge, C. 1901. Uber die darstellung willlctirlicher functionen und die interpolation zwischen aquidistanten ordinaten. Z. Angew. Math. Phys. 46, 224-243.

Google Scholar

[52]

Sacks, J., Welch, W.J., Mitchell, T.J., and Wyrm, H.P. 1989. Design and analysis of computer experiments. Statistical Science, 4, 409-435.

Crossref

Google Scholar

[53]

Sargent, R.G. 1991. Research issues in metamodeling. Proceedings of the 1991 Winter Simulation Conference, t~.L. Nelgon, W. D. Kelton, G.M. Clark ed~.), ggg-g93.

Digital Library

Google Scholar

[54]

Schruben, L.W. and Cogliano, V.J. 1987. An experimental procedure for simulation response surface model identification. Communications of the ACM, 30, 716-730.

Digital Library

Google Scholar

[55]

Schruben, L.W. and Margolin, B.H. 1978. Pseudorandom number assignment in statistically designed simulation and distribution sampling experiments. Journal of the American Statistical Association, 73, 504-525.

Crossref

Google Scholar

[56]

Schumaker, L.L. 1981. Spline Functions: Basic Theory. New York: John Wiley and Sons.

Google Scholar

[57]

Sheffe~, H. 1954. The Analysis of Variance. New York: John Wiley and Sons.

Google Scholar

[58]

Strang, G. 1989. Wavelets and dilation equations: a brief introduction. SIAM Review, 31,614-627.

Digital Library

Google Scholar

[59]

Suri, R. and Diehl, G.W. 1985. MANUPLAN: a precursor to simulation for complex manufacturing systems. Proceedings of the 1985 Winter Simulation Conference.

Digital Library

Google Scholar

[60]

Suri, R. and Diehl, D.W. 1987. Rough cut modelling: an alternative to simulation. CIM Rev/ew, 3, 25-32.

Digital Library

Google Scholar

[61]

Taguchi, G. 1959. Linear graphs for orthogonal arrays and their application to experimental design, with the aid of various techniques. Reports of Statistical Application Research, Union of Japanese Scientists and Engineers, 6, 4, 1-43.

Google Scholar

[62]

Taguchi, G. 1960. Tables of orthogonal arrays and linear graphs. Reports of Statistical Application Research, Union of Japanese Scientists and Engineers, 6, 5, 1-52.

Google Scholar

[63]

Tew, J.D. 1992. Correlated replicates for first-order metamodel estimation in simulation experiments. Transactions of The Soc&ty for Computer Simulation, 8, 218-244.

Google Scholar

[64]

Tew, J.D., and Crenshaw, M.D. 1990. Heuristic diagno.,;tics for the presence of pure error in computer simulation models. Proceedings of the 1990 Winter Simulation Conference, O. Balci, R.P. Sadowski, and R.E. Nance eds. 347-356.

Digital Library

Google Scholar

[65]

Tew, J.D. and Wilson, J.R. 1988. Estimating simulation metamodels using integrated variance reduction techniques. 1988 Proceedings of the Statistical Computing Section, Alexandria, Va: American Statistical Association, 28-32.

Google Scholar

[66]

Tew, J.D. and Wilson, J.R. 1992. Validation of simulation analysis methods for the Schruben-Margolin correlaltion induction strategy. Operations Research, 40, 87-103.

Digital Library

Google Scholar

[67]

Thompson, J. R. and Tapia, R.A. 1990. Nonpararm'tric Function Estimation, Modeling, and Simulation. Philadelphia: Society for Industrial and Applied Mathematics.

Google Scholar

[68]

Welch, P.D. 1990. Simulation and regression: dealing with the assumption of a common error variance. Proceedings of the 1990 Winter Simulation Conference, O. Balci, R.P. Sadowski, and R.E. Nance eds. 392-394.

Digital Library

Google Scholar

[69]

Wu, C.F.J. and Chen, Y.Y. 1992. A graph-aided method for planning two-level experiments when certain interactions are important. Technometrics, 34, 162-175.

Digital Library

Google Scholar

Cited By

View all

Godi N(2024)Constructal Solid and Perforated Fin Installation in a Combined Microchannel Heat Sink for Maximum Heat TransferJournal of The Institution of Engineers (India): Series C10.1007/s40032-023-01006-y105:1(17-30)Online publication date: 3-Jan-2024
https://doi.org/10.1007/s40032-023-01006-y
Barton R(2023)Tutorial: Basics of MetamodelingProceedings of the Winter Simulation Conference10.5555/3643142.3643267(1516-1530)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643267
Barton R(2023)Tutorial: Basics of Metamodeling2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10408331(1516-1530)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10408331
Show More Cited By

Index Terms

Metamodels for simulation input-output relations
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Mathematics of computing
  1. Mathematical analysis
    1. Functional analysis
      1. Approximation
    2. Numerical analysis
      1. Computations on polynomials

Recommendations

Testing Metamodels
ECMDA-FA '08: Proceedings of the 4th European conference on Model Driven Architecture: Foundations and Applications

In this paper, we deal with errors in metamodels. Metamodels define the abstract syntax of modeling languages. They play a central role in the Model-Driven Architecture. Other artifacts like models or tools are based on them and have to be changed if ...
Semi-automatic generation of metamodels from model sketches
ASE '13: Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering

Traditionally, metamodeling is an upfront activity performed by experts for defining modeling languages. Modeling tools then typically restrict modelers to using only constructs defined in the metamodel. This is inappropriate when users want to sketch ...
Towards a comparative analysis of meta-metamodels
SPLASH '11 Workshops: Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE! 2011, AOOPES'11, NEAT'11, & VMIL'11

A cornerstone in Domain-Specific Modeling is the definition of modeling languages. A widely used method to formalize domain-specific languages is the metamodeling approach. There are a huge number of metamodeling languages. The choice of a suitable ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WSC '92: Proceedings of the 24th conference on Winter simulation

December 1992

1410 pages

ISBN:0780307984

DOI:10.1145/167293

Chairman:
Robert C. Crain
Wolverine Software Corporation, Annandale, VA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1992

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

WSC90

Sponsor:

IIE
IEEE-SMCS
ASA
ACM
ORSA
SCS
TIMS
SIGSIM
IEEE-CS
NIST

WSC90: 1990 Winter Simulation Conference

December 13 - 16, 1992

Virginia, Arlington, USA

Acceptance Rates

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

109
Total Citations
View Citations
1,168
Total Downloads

Downloads (Last 12 months)69
Downloads (Last 6 weeks)12

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Godi N(2024)Constructal Solid and Perforated Fin Installation in a Combined Microchannel Heat Sink for Maximum Heat TransferJournal of The Institution of Engineers (India): Series C10.1007/s40032-023-01006-y105:1(17-30)Online publication date: 3-Jan-2024
https://doi.org/10.1007/s40032-023-01006-y
Barton R(2023)Tutorial: Basics of MetamodelingProceedings of the Winter Simulation Conference10.5555/3643142.3643267(1516-1530)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643267
Barton R(2023)Tutorial: Basics of Metamodeling2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10408331(1516-1530)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10408331
Wu GHo YHu EWu TShen C(2023)Optimal Graphene Heat Pipe Parameters for Enhanced Battery Thermal Management through Global Optimization2023 IEEE 23rd International Conference on Nanotechnology (NANO)10.1109/NANO58406.2023.10231239(431-436)Online publication date: 2-Jul-2023
https://doi.org/10.1109/NANO58406.2023.10231239
Barton R(2022)TutorialProceedings of the Winter Simulation Conference10.5555/3586210.3586316(1268-1282)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586316
Bahrami PSahari Moghaddam FJames L(2022)A Review of Proxy Modeling Highlighting Applications for Reservoir EngineeringEnergies10.3390/en1514524715:14(5247)Online publication date: 20-Jul-2022
https://doi.org/10.3390/en15145247
Barton R(2022)Tutorial: Metamodeling for Simulation2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015386(1268-1282)Online publication date: 11-Dec-2022
https://doi.org/10.1109/WSC57314.2022.10015386
Hou CBehdinan K(2022)Dimensionality Reduction in Surrogate Modeling: A Review of Combined MethodsData Science and Engineering10.1007/s41019-022-00193-57:4(402-427)Online publication date: 21-Aug-2022
https://doi.org/10.1007/s41019-022-00193-5
Jalal HTrikalinos TAlarid-Escudero F(2021)BayCANN: Streamlining Bayesian Calibration With Artificial Neural Network MetamodelingFrontiers in Physiology10.3389/fphys.2021.66231412Online publication date: 25-May-2021
https://doi.org/10.3389/fphys.2021.662314
Gauder DBiehler MGölz JStampfer BBöttger DHäfner BWolter BSchulze VLanza G(2021)Development of a methodical approach for uncertainty quantification and meta-modeling of surface hardness in white layers of longitudinal turned AISI4140 surfacestm - Technisches Messen10.1515/teme-2021-003788:11(661-673)Online publication date: 16-Oct-2021
https://doi.org/10.1515/teme-2021-0037
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Testing Metamodels

Semi-automatic generation of metamodels from model sketches

Towards a comparative analysis of meta-metamodels