Abstract
In shape optimization, design improvements significantly depend on the dimension and variability of the design space. High dimensional and variability spaces are more difficult to explore, but also usually allow for more significant improvements. The assessment and breakdown of design-space dimensionality and variability are therefore key elements to shape optimization. A linear method based on the principal component analysis (PCA) has been developed in earlier research to build a reduced-dimensionality design-space, resolving the 95% of the original geometric variance. The present work introduces an extension to more efficient nonlinear approaches. Specifically the use of Kernel PCA, Local PCA, and Deep Autoencoder (DAE) is discussed. The methods are demonstrated for the design-space dimensionality reduction of the hull form of a USS Arleigh Burke-class destroyer. Nonlinear methods are shown to be more effective than linear PCA. DAE shows the best performance overall.
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Acknowledgments
The work is supported by the US Office of Naval Research Global, NICOP grant N62909-15-1-2016, under the administration of Dr. Woei-Min Lin, Dr. Salahuddin Ahmed, and Dr. Ki-Han Kim, and by the Italian Flagship Project RITMARE. The research is performed within NATO STO Task Group AVT-252 Stochastic Design Optimization for Naval and Aero Military Vehicles. The authors wish to thank Prof. Frederick Stern and Dr. Manivannan Kandasamy of The University of Iowa for inspiring the current research on nonlinear dimensionality reduction methods.
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D’Agostino, D., Serani, A., Campana, E.F., Diez, M. (2018). Nonlinear Methods for Design-Space Dimensionality Reduction in Shape Optimization. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_11
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DOI: https://doi.org/10.1007/978-3-319-72926-8_11
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