Parameter screening using impact factors and surrogate-based ANOVA techniques
Parameter screening using impact factors and surrogate-based ANOVA techniques
This paper introduces the concept of parameter impact factors in order to screen important parameters in high dimensional design optimization problems which make use of computationally expensive high fidelity simulation models. Based on a snapshot dataset obtained by evaluating design points produced by Design of Experiments techniques, a simple concept of parameter impact factors is introduced and calculated to obtain preliminary estimates on the importance of parameters in the simulation results. Combined with parallel tuning of hyperparameters used in Gaussian process surrogate models and ANOVA techniques using the progressively built surrogate models, a more accurate estimation on the impact of different parameters can be achieved. Less important parameters can then be fixed in order to reduce the dimensionality of the problem to make the problem more tractable within given computational budget and time constraints.
Song, Wenbin
390dc209-bfcb-4986-8362-c25b40272307
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
8 September 2006
Song, Wenbin
390dc209-bfcb-4986-8362-c25b40272307
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
Song, Wenbin and Keane, Andy
(2006)
Parameter screening using impact factors and surrogate-based ANOVA techniques.
11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, USA.
06 - 08 Sep 2006.
8 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper introduces the concept of parameter impact factors in order to screen important parameters in high dimensional design optimization problems which make use of computationally expensive high fidelity simulation models. Based on a snapshot dataset obtained by evaluating design points produced by Design of Experiments techniques, a simple concept of parameter impact factors is introduced and calculated to obtain preliminary estimates on the importance of parameters in the simulation results. Combined with parallel tuning of hyperparameters used in Gaussian process surrogate models and ANOVA techniques using the progressively built surrogate models, a more accurate estimation on the impact of different parameters can be achieved. Less important parameters can then be fixed in order to reduce the dimensionality of the problem to make the problem more tractable within given computational budget and time constraints.
Text
song_06.pdf
- Accepted Manuscript
More information
Published date: 8 September 2006
Additional Information:
AIAA 2006-7088
Venue - Dates:
11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, USA, 2006-09-06 - 2006-09-08
Identifiers
Local EPrints ID: 41986
URI: http://eprints.soton.ac.uk/id/eprint/41986
PURE UUID: dacf6963-f52c-4d2f-96f3-66f662cc1737
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Date deposited: 25 Oct 2006
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
Wenbin Song
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