Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications

Authors

Vladik Kreinovich, The University of Texas at El PasoFollow
J. Beck
Carlos M. Ferregut, The University of Texas at El PasoFollow
A. Sanchez
George R. Keller, The University of Texas at El PasoFollow
Matthew G. Averill
Scott A. Starks, The University of Texas at El PasoFollow

Publication Date

12-2005

Comments

UTEP-CS-04-23c.

Original version published in Proceedings of the Workshop on Reliable Engineering Computing, Savannah, Georgia, September 15-17, 2004, pp. 139-160; detailed version published in Reliable Computing, 2007, Vol. 13, No. 1, pp. 25-69.

Abstract

In engineering applications, we need to make decisions under uncertainty. Traditionally, in engineering, statistical methods are used, methods assuming that we know the probability distribution of different uncertain parameters. Usually, we can safely linearize the dependence of the desired quantities y (e.g., stress at different structural points) on the uncertain parameters xi - thus enabling sensitivity analysis. Often, the number n of uncertain parameters is huge, so sensitivity analysis leads to a lot of computation time. To speed up the processing, we propose to use special Monte-Carlo-type simulations.