Reduced Order Modeling

Thursday, September 7, 2017 - 1:50pm - 2:25pm
Aaron Luttman (National Security Technologies, LLC)
The basic process for reduced order modeling is to generate or measure training data, use the training data to construct a reduced order model (ROM) forthe data space, generate or measure test data, then project the test data onto to the ROM. In order to quantify uncertainties with respect to the use of the ROM, it is necessary to understand the errors and uncertainties associated with each step in the process, but simply propagating errors from one step to the next in quadrature overestimates the total error in many applications.
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