Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction

Technical Report · Sat Jun 01 00:00:00 EDT 1991

DOI:https://doi.org/10.2172/5836957· OSTI ID:5836957

Morris, M. D.; Mitchell, T. J. ^[1]; Ylvisaker, D. ^[2]

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1. Oak Ridge National Lab., TN (USA)
2. California Univ., Los Angeles, CA (USA). Dept. of Mathematics

The work of Currin et al. and others in developing fast predictive approximations'' of computer models is extended for the case in which derivatives of the output variable of interest with respect to input variables are available. In addition to describing the calculations required for the Bayesian analysis, the issue of experimental design is also discussed, and an algorithm is described for constructing maximin distance'' designs. An example is given based on a demonstration model of eight inputs and one output, in which predictions based on a maximin design, a Latin hypercube design, and two compromise'' designs are evaluated and compared. 12 refs., 2 figs., 6 tabs.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE, Washington, DC (USA)
• DOE Contract Number: AC05-84OR21400
• OSTI ID: 5836957
• Report Number(s): ORNL/TM-11699; ON: DE91013147
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
BOREHOLES
MATHEMATICAL MODELS
SURFACES
ALGORITHMS
COMPUTERIZED SIMULATION
DESIGN
HYPERCUBE COMPUTERS
INTERPOLATION
OPTIMIZATION
RANDOMNESS
CAVITIES
COMPUTERS
MATHEMATICAL LOGIC
NUMERICAL SOLUTION
SIMULATION
990200* - Mathematics & Computers