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

Structural and Multidisciplinary Optimization volume 65, Article number: 189 (2022) Cite this article

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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References

  • Acar E, Bayrak G, Jung Y, Lee I, Ramu P, Ravichandran SS (2021) Modeling, analysis, and optimization under uncertainties: a review. Struct Multidisc Optim 64(5):2909–2945

    MathSciNet  Article  Google Scholar 

  • Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65

    Article  Google Scholar 

  • Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 91:464–471

    Article  Google Scholar 

  • Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1–22

    Article  Google Scholar 

  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27

  • Haghighat E, Raissi M, Moure A, Gomez H, Juanes R (2021) A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics. Comput Methods Appl Mech Eng 379:113741

    MathSciNet  Article  Google Scholar 

  • Han T, Liu C, Wu R, Jiang D (2021) Deep transfer learning with limited data for machinery fault diagnosis. Appl Soft Comput 103:107150

    Article  Google Scholar 

  • Islam Z, Abdel-Aty M, Cai Q, Yuan J (2021) Crash data augmentation using variational autoencoder. Accid Anal Prev 151:105950

    Article  Google Scholar 

  • Kaur K, Garg A, Cui X, Singh S, Panigrahi BK (2021) Deep learning networks for capacity estimation for monitoring SOH of Li-ion batteries for electric vehicles. Int J Energy Res 45(2):3113–3128

    Article  Google Scholar 

  • Kohar CP, Greve L, Eller TK, Connolly DS, Inal K (2021) A machine learning framework for accelerating the design process using CAE simulations: an application to finite element analysis in structural crashworthiness. Comput Methods Appl Mech Eng 385:114008

    MathSciNet  Article  Google Scholar 

  • Laubscher R, Rousseau P (2021) An integrated approach to predict scalar fields of a simulated turbulent jet diffusion flame using multiple fully connected variational autoencoders and MLP networks. Appl Soft Comput 101:107074

    Article  Google Scholar 

  • Li X, Yang Y, Li L, Zhao G, He N (2020) Uncertainty quantification in machining deformation based on Bayesian network. Reliab Eng Syst Saf 203:107113

    Article  Google Scholar 

  • Lv JJ, Shao XH, Huang JS, Zhou XD, Zhou X (2019) Data augmentation for face recognition. Neurocomputing 230:184–196

    Article  Google Scholar 

  • Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  • Ray MH, Mongiardini M, Plaxico CA (2012) Quantitative methods for assessing similarity between computational results and full-scale crash tests. In: Proceedings of the 91th Annual Meeting of the Transportation Research Board, Washington, pp 1–21

  • Salamon J, Bello JP (2017) Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett 24(3):279–283

    Article  Google Scholar 

  • Shin W, Bu SJ, Cho SB (2020) 3D-convolutional neural network with generative adversarial network and autoencoder for robust anomaly detection in video surveillance. Int J Neural Syst 30(6):2050034

    Article  Google Scholar 

  • Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1–48

    Article  Google Scholar 

  • Srivastava N, Hinton G, Krizhevsky A, Sustskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  • Wang C, Xu C, Yao X, Tao D (2019) Evolutionary generative adversarial networks. IEEE Trans Evol Comput 23(6):921–934

    Article  Google Scholar 

  • Xu X, Li J, Yang Y, Shen F (2020) Toward effective intrusion detection using log-cosh conditional variational autoencoder. IEEE Internet Things J 8(8):6187–6196

    Article  Google Scholar 

  • Yang Y, Perdikaris P (2019) Adversarial uncertainty quantification in physics-informed neural networks. J Comput Phys 394:136–152

    MathSciNet  Article  Google Scholar 

  • Yang Y, Zheng K, Wu C, Yang Y (2019) Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network. Sensors 19(11):2528

    Article  Google Scholar 

  • Yang S, Zhang Y, Wang H, Li P, Hu X (2020) Representation learning via serial robust autoencoder for domain adaption. Expert Syst Appl 160:113635

    Article  Google Scholar 

  • Yılmaz İ, Yelek İ, Özcanan S, Atahan AO, Hiekmann JM (2021) Artificial neural network metamodeling-based design optimization of a continuous motorcyclists protection barrier system. Struct Multidisc Optim 64(6):4305–4323

    Article  Google Scholar 

  • Yonekura K, Suzuki K (2021) Data-driven design exploration method using conditional variational autoencoder for airfoil design. Struct Multidisc Optim 64(2):613–624

    Article  Google Scholar 

  • Yoo Y, Jung UJ, Han YH, Lee J (2021) Data augmentation-based prediction of system level performance under model and parameter uncertainties: role of designable generative adversarial networks (DGAN). Reliab Eng Syst Saf 206:107316

    Article  Google Scholar 

  • Zavrak S, Iskefiyeli M (2020) Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8:108346–108358

    Article  Google Scholar 

  • Zhong SS, Fu S, Lin L (2019) A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement 137:435–453

    Article  Google Scholar 

  • Zhu L, Chen Y, Ghamisi P, Benediktsson JA (2018) Generative adversarial networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(9):5046–5063

    Article  Google Scholar 

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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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Authors and Affiliations

  1. School of Mechanical Engineering, Yonsei University, Seoul, 03722, Korea

    Yeongmin Yoo & Jongsoo Lee

  2. Department of Automotive Engineering, Gwangju University, Gwangju, 61743, Korea

    Chang-Kyu Park

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  1. Yeongmin Yoo
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  2. Chang-Kyu Park
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  3. Jongsoo Lee
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Correspondence to Jongsoo Lee.

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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