Design and analysis of 'noisy' computer experiments
Design and analysis of 'noisy' computer experiments
Recently there has been a growing interest in using response surface techniques to expedite the global optimization of functions calculated by long running computer codes. The literature in this area commonly assumes that the objective function is a smooth, deterministic function of the inputs. Yet it is well known that many computer simulations -- especially those of computational fluid and structural dynamics codes -- often display what one might call 'numerical noise': rather than lying on a smooth curve, results appear to contain a random scatter about a smooth trend. This paper extends previous optimization methods based on the interpolating method of kriging to the case of such 'noisy' computer experiments. Firstly, we review how the kriging interpolation can be modified to filter out numerical noise. We then show how to adjust the estimate of the error in a kriging prediction so that previous approaches to optimization, such as the method of maximizing the expected improvement, continue to work effectively. We introduce the problems associated with noise and demonstrate our approach using computational fluid dynamics based problems.
CFD, kriging, optimization
2331-2339
Forrester, A.I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
October 2006
Forrester, A.I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Forrester, A.I.J., Keane, A.J. and Bressloff, N.W.
(2006)
Design and analysis of 'noisy' computer experiments.
AIAA Journal, 44 (10), .
(doi:10.2514/1.20068).
Abstract
Recently there has been a growing interest in using response surface techniques to expedite the global optimization of functions calculated by long running computer codes. The literature in this area commonly assumes that the objective function is a smooth, deterministic function of the inputs. Yet it is well known that many computer simulations -- especially those of computational fluid and structural dynamics codes -- often display what one might call 'numerical noise': rather than lying on a smooth curve, results appear to contain a random scatter about a smooth trend. This paper extends previous optimization methods based on the interpolating method of kriging to the case of such 'noisy' computer experiments. Firstly, we review how the kriging interpolation can be modified to filter out numerical noise. We then show how to adjust the estimate of the error in a kriging prediction so that previous approaches to optimization, such as the method of maximizing the expected improvement, continue to work effectively. We introduce the problems associated with noise and demonstrate our approach using computational fluid dynamics based problems.
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Submitted date: 14 September 2005
Published date: October 2006
Keywords:
CFD, kriging, optimization
Identifiers
Local EPrints ID: 26794
URI: http://eprints.soton.ac.uk/id/eprint/26794
ISSN: 0001-1452
PURE UUID: 1f2bd5d5-a508-40c2-8f05-d285d1c4f892
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Date deposited: 19 Apr 2006
Last modified: 16 Mar 2024 02:53
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