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Surfpack:
A Library of Multidimensional Surface Fitting Methods 

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Anthony A. Giunta
Mark Richards
Eric Cyr
Shannon L. Brown
Laura P. Swiler
Michael S. Eldred
Keith R. Dalbey
Brian M. Adams

Contact:
Brian M. Adams
Sandia National Laboratories
P.O. Box 5800, Mail Stop 1318
Albuquerque, NM 87185-1318
Email: surfpack-developers@development.sandia.gov
Web: http://dakota.sandia.gov/packages/surfpack

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License:

Surfpack is distributed under the GNU Lesser General Public
License. It is free software; you can redistribute it and/or modify it
under the terms of the GNU Lesser General Public License as published
by the Free Software Foundation; either version 2.1 of the License, or
(at your option) any later version.  A copy of this license is
included in the GNU_LICENSE_INFO file, and is also available online
from http://www.gnu.org/licenses/lgpl.html.  Also refer to the
COPYRIGHT file for important U.S. Government legal notices.

The Surfpack distribution contains libraries which are available separately
under compatible licensing terms.  In particular, many of the libraries in src
are available separately under either the GNU Lesser General Public License
(LGPL).

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Overview:

The Surfpack library provides a collection of surface fitting methods and
quality-of-fit metrics that can be used to quantify the error in the resulting
approximations.  Currently supported data-fitting algorithms include
least-squares linear regression, multivariate adaptive regression splines
(MARS), Kriging interpolation, artificial neural networks, and radial basis
function networks.  These algorithms are available both as a C++ library and as
a standalone program with a simple shell interface.

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Surfpack References:

General Information on Surfpack
Web: http://dakota.sandia.gov/packages/surfpack
Surfpack User's Manual (available on the website)


Technical Documents:

Giunta, A. A., Swiler, L. P., Brown, S. L., Eldred, M. S., Richards, M. D., 
and Cyr, E. C., "The Surfpack Software Library for Surrogate Modeling of 
Sparse Irregularly Spaced Multidimensional Data," (to appear) 11th AIAA/ISSMO
Multidisciplinary Analysis and Optimization Conference, AIAA Paper 2006-7049,
Portsmouth, VA, 2006.
