Uncertainty Evaluation Through Mapping Identification in Intensive Dynamic Simulations

We study how the dependence of a simulation output on an uncertain parameter can be determined, when simulations are computationally expensive and so can only be run for very few parameter values. Specifically, the methodology that we develop--known as the probabilistic collocation method (PCM)--permits selection of these few parameter values, so that the mapping between the parameter and output can be approximated well over the likely parameter values, using a low-order polynomial. We give several new analyses concerning the ability of PCM to predict the mapping structure as well as output statistics. We also develop a holistic methodology for the typical case that the uncertain parameter's probability distribution is unknown, and instead only depictive moments or sample data (which possibly depend on known regressors) are available. Finally, we pursue application of PCM to weather-uncertainty evaluation in air traffic flow management in some detail, and briefly introduce applica...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Yan Wan, Sandip Roy, Bernard C. Lesieutre
• Keywords: Artificial Intelligence, Parameter, Parameter Values, TSMC 2010, Uncertain Parameter