Deep learning-based efficient metamodeling via domain knowledge-integrated designable data augmentation with transfer learning: application to vehicle crash safety

Yeongmin Yoo, Chang Kyu Park, Jongsoo Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Deep learning-based metamodeling can approximate complex engineering systems based on design data, but it has limitations in acquiring a large amount of data through experiments or simulations. When the design data for metamodeling are insufficient, data can be generated through generative models of deep learning. This study proposes a deep learning-based efficient metamodeling method called domain knowledge-integrated designable data augmentation (DDA) with transfer learning for engineering design. The DDA is a metamodel that applies an inverse generator to existing data augmentation algorithms. Virtual responses can be generated using a small number of actual responses to predict the performance of an engineering system and estimate the design variables that affect the generated virtual responses. Moreover, a rapid and accurate design can be achieved by applying transfer learning and domain knowledge-based learning to DDA. The proposed algorithm was applied to the design of a bumper considering vehicle crash safety. As a result, virtual responses that were approximately 95% similar to the actual responses were generated, and design solutions were derived. In addition, the validity of the proposed algorithm was verified by comparing it with existing metamodels. Graphical abstract: [Figure not available: see fulltext.].

Original languageEnglish
Article number189
JournalStructural and Multidisciplinary Optimization
Volume65
Issue number7
DOIs
Publication statusPublished - 2022 Jul

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Optimization
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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