TITLE:

QUANTIFICATION OF ALEATORIC AND EPISTEMIC UNCERTAINTY IN COMPUTATIONAL MODELS OF COMPLEX SYSTEMS

DATE:

Friday, November 5th, 2010

TIME:

3:30 PM

LOCATION:

GMCS 214

SPEAKER:

Angel Urbina, Validation and Uncertainty Department, Sandia National Laboratories

ABSTRACT:

For complex engineering systems, testing-based assessment is increasingly sought to be replaced by simulations using detailed computational models. This is due to a lack of system experimental data and/or resources to conduct these experiments. Components, which are part of the system, are usually cheaper to build and test. This lack of system data and the complexity of the system being model leads to a need to build models in a building-block or hierarchical manner. This approach takes advantage of data at the component level by guiding the development of each component model. These models are then coupled to form the system model. Quantification of uncertainty in a system response is required to establish the confidence in representing the actual system behavior. To be accurate, this quantification needs to include both aleatoric and epistemic uncertainty. This presentation shows a framework based on Bayes networks that uses the available data at multiple levels of complexity (i.e. components, subsystem, etc) and allows quantification and propagation of both types of uncertainty in a system model prediction. Methods, to incorporate epistemic uncertainty given in terms of intervals on a model parameter, are presented and a numerical example demonstrating the approach is shown.

HOST:

Satchi Venkataraman

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