Safety Margins for Conservative Surrogates

[Felipe A. C. Viana]

Dear all,

Here it is a reference on conservative surrogates:

F. A. C. Viana, V. Picheny, and R.T. Haftka, "Safety Margins for
Conservative Surrogates," in: 8th World Congress on Structural and
Multidisciplinary Optimization, Lisbon, Portugal, June 1-5, 2009.

Using surrogate models for learning or optimization creates a risk
associated to the fitting error that must be accounted for.
Conservative surrogates are metamodels designed to safely estimate the
actual response of the system. In this work we use safety margins to
generate conservative surrogates. Given a desired level of
conservativeness (percentage of safe predictions), we propose the use
of cross-validation for estimating the required safety margin. We also
explore how multiple surrogates and cross-validation can be used to
minimize the loss of accuracy inherent in conservative surrogates. The
approach was tested on two algebraic examples for ten basic surrogates
including different instances of kriging, polynomial response surface,
radial basis neural networks and support vector regression surrogates.
For these examples we found that cross-validation (i) is effective for
selecting the safety margin; and (ii) allows us to select a surrogate
with the best compromise between conservativeness and loss of
accuracy.

You can find more about it online:
http://fchegury.googlepages.com

All the best,
Felipe A.C. Viana