A Resampling Based Approach to Optimal Experimental Design for Computer Analysis of a Complex System

Conference · Wed Aug 04 04:00:00 EDT 1999

OSTI ID:9503

Rutherford, Brian ^[1]

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1. Sandia National Laboratories

The investigation of a complex system is often performed using computer generated response data supplemented by system and component test results where possible. Analysts rely on an efficient use of limited experimental resources to test the physical system, evaluate the models and to assure (to the extent possible) that the models accurately simulate the system order investigation. The general problem considered here is one where only a restricted number of system simulations (or physical tests) can be performed to provide additional data necessary to accomplish the project objectives. The levels of variables used for defining input scenarios, for setting system parameters and for initializing other experimental options must be selected in an efficient way. The use of computer algorithms to support experimental design in complex problems has been a topic of recent research in the areas of statistics and engineering. This paper describes a resampling based approach to form dating this design. An example is provided illustrating in two dimensions how the algorithm works and indicating its potential on larger problems. The results show that the proposed approach has characteristics desirable of an algorithmic approach on the simple examples. Further experimentation is needed to evaluate its performance on larger problems.

• Research Organization: Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 9503
• Report Number(s): SAND99-0363C
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
COMPUTER-AIDED DESIGN
COMPUTERIZED SIMULATION
EXPERIMENTAL DESIGN
LARGE-SCALE SIMULATION
MECHANICAL STRUCTURES
PERFORMANCE
STATISTICS
UNCERTAINTY ANALYSIS