Gradient-Enhanced Uncertainty Propagation

Thursday, June 2, 2011 - 11:15am - 12:15pm
Lind 305
Mihai Anitescu (Argonne National Laboratory)
In this work we discuss an approach for uncertainty propagation
through computationally expensive physics simulation
codes. Our approach incorporates gradient information information
to provide a higher quality surrogate with fewer simulation
results compared with derivative-free approaches.

We use this information in two ways: we fit a polynomial or Gaussian process model (surrogate) of the system response. In a third approach we hybridize the techniques where a Gaussian process with polynomial mean is fit resulting in an improvement of both techniques. The surrogate coupled with input uncertainty information provides a complete uncertainty approach when
the physics simulation code can be run at only a small number
of times. We discuss various algorithmic choices such as polynomial basis and covariance kernel. We demonstrate our findings on synthetic
functions as well as nuclear reactor models.
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