389640 Multiscale Modeling with Dynamic Discrepancy

Monday, November 17, 2014
Galleria Exhibit Hall (Hilton Atlanta)
David Mebane, Mechanical and Aerospace Department, West Virginia University, Morgantown, WV

The dynamic discrepancy methodology is an approach to multi-scale modeling based on quantification of uncertainty in scale-bridging.  It is a reduced order approach using stochastic functions to efficiently replace model variability removed when reducing problem complexity.  It can be utilized in conjunction with rigorous reduced-order strategies such as the proper orthogonal decomposition.  The stochastic character of the method leads to the opportunity for machine learning in reduced model training.  A demonstration of the method on problems in modeling of carbon capture systems will be presented, using both hypothetical and real data sets.

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