Application of domain-adaptive convolutional variational autoencoder for stress-state prediction

Sang Min Lee, Sang Youn Park, Byoung Ho Choi

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Applying data-driven methods such as deep learning in material mechanics is challenging because producing a sufficiently large, labeled dataset is costly resource-wise. This paper outlines a new approach to overcoming this difficulty by transferring knowledge from a source domain of finite-element-analysis data to a target domain of real-world test-specimen images so that a model capable of accurate and robust predictions in both domains may be constructed. To achieve this transfer of knowledge, discrepancy-based unsupervised domain adaptation is adopted into a convolutional variational autoencoder structure. To evaluate the proposed approach, a four-point bending experiment was conducted on 6061 aluminum alloy and 316 stainless steel to produce 550 unlabeled target-domain data images. The same bending situation was analyzed using the finite-element method implemented in the commercial software package ABAQUS to produce 6000 labeled, source-domain data images. The proposed domain-adaptive convolutional variational autoencoder was trained using the maximum mean discrepancy method on the target- and the source-domain data. The predictions using the domain-adapted convolutional variational autoencoder were relatively more accurate than those using the model trained only on the source domain. It is expected that the proposed approach can address the scarcity of labeled data in various applications of material mechanics and provide a base technology for the development of various data-driven approaches.

Original languageEnglish
Article number108827
JournalKnowledge-Based Systems
Publication statusPublished - 2022 Jul 19

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1A2C2012958 ). It was also supported by a Korea University fund.

Publisher Copyright:
© 2022 Elsevier B.V.


  • Convolutional neural network
  • Deep learning
  • Four-point bending
  • Stress analysis
  • Unsupervised domain adaptation
  • Variational autoencoder

ASJC Scopus subject areas

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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