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2019 | OriginalPaper | Chapter

Augmented Design-Space Exploration by Nonlinear Dimensionality Reduction Methods

Authors : Danny D’Agostino, Andrea Serani, Emilio Fortunato Campana, Matteo Diez

Published in: Machine Learning, Optimization, and Data Science

Publisher: Springer International Publishing

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Abstract

The paper presents the application of nonlinear dimensionality reduction methods to shape and physical data in the context of hull-form design. These methods provide a reduced-dimensionality representation of the shape modification vector and associated physical parameters, allowing for an efficient and effective augmented design-space exploration. The data set is formed by shape coordinates and hydrodynamic performance (based on potential flow simulations) obtained by Monte Carlo sampling of a 27-dimensional design space. Nonlinear extensions of the principal component analysis (PCA) are applied, namely kernel PCA, local PCA and a deep autoencoder. The application presented is a naval destroyer sailing in calm water. The reduced-dimensionality representation of shape and physical parameters is set to provide a normalized mean square error smaller than 5%. Nonlinear methods outperform the standard PCA, indicating significant nonlinear interactions in the data structure. The present work is an extension of the authors’ research [1] where only shape data were considered.

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Metadata
Title
Augmented Design-Space Exploration by Nonlinear Dimensionality Reduction Methods
Authors
Danny D’Agostino
Andrea Serani
Emilio Fortunato Campana
Matteo Diez
Copyright Year
2019
Book
Machine Learning, Optimization, and Data Science

Print ISBN: 978-3-030-13708-3

Electronic ISBN: 978-3-030-13709-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-13709-0_13

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