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

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.

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Acknowledgements

This work was supported by the National Research Foundation of Korea [Grant No. 2022R1A2C2011034]. This work was supported by 'Development of Prognostics and Health Management Based on Multivariate Analysis of Autonomous Vehicle Parts [Grant No. 20018208].

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Correspondence to Jongsoo Lee.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

Replication of results

The related codes, including the architecture of neural networks as well as datasets used in the current study, are available in the Github repository, with a link of https://github.com/YeongminYoo/DDATL.

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Yoo, Y., Park, CK. & Lee, J. Deep learning-based efficient metamodeling via domain knowledge-integrated designable data augmentation with transfer learning: application to vehicle crash safety. Struct Multidisc Optim 65, 189 (2022). https://doi.org/10.1007/s00158-022-03290-1

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  • DOI: https://doi.org/10.1007/s00158-022-03290-1

Keywords

  • Data augmentation
  • Transfer learning
  • Domain knowledge
  • Inverse generator
  • Metamodeling
  • Vehicle crash safety