Orthogonal arrays for computer experiments to assess important inputs
- Leslie M.
The topic of this paper is experiment planning, particularly fractional factorial designs or orthogonal arrays, for computer experiments to assess important inputs. The work presented in the paper is motivated by considering a non-stochastic computer simulation which has many inputs and which can, in a reasonable period of time, be run thousands of times. With many inputs, information that allows focus on a subset of important inputs is valuable. The characterization of 'importance' is expected to follow suggestions in McKay (1995) or McKay, et al. (1992). This analysis approach leads to considering factorial experiment designs. Inputs are associated with a finite number of discrete values, referred to as levels, so if each input has K levels and there are p inputs then there are K{sup P} possible distinct runs which constitute the K{sup P} factorial design space. The suggested size of p has been 35 to 50 so that even with K=2 the complete 2{sup P} factorial design space would not be run. Further, it is expected that the complexity of the simulation code and discrete levels possibly associated with equi-probable intervals from the input distribution make it desirable to consider more than 2 level inputs. Inputs levels of 5 and 7 have been investigated. In this paper, orthogonal array experiment designs, which are subsets of factorial designs also referred to as fractional factorial designs, are suggested as candidate experiments which provide meaningful basis for calculating and comparing R{sup 2} across subsets of inputs.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 976110
- Report Number(s):
- LA-UR-02-1434; TRN: US201009%%545
- Resource Relation:
- Conference: Submitted to: 6th International Conference on Probabilistic Safety Assessment and Management, Puerto Rico
- Country of Publication:
- United States
- Language:
- English
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