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


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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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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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

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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).

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