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Cross-resolution topology optimization for geometrical non-linearity by using deep learning

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Abstract

In this paper, a cross-resolution acceleration method for topology optimization is proposed based on deep learning aiming at achieving precise and high-efficiency geometrically non-linear structure design. We develop a cross-resolution Pix2pix neural network (CR-Pix2pix NN) to build the high-dimensional mapping between the low-resolution intermediate configuration (IC) and the corresponding high-resolution optimized configuration. Architecture of CR-Pix2pix NN is composed of a cross-resolution generator and Markovian discriminator. The cross-resolution geometrically non-linear dataset utilized to train the deep learning model is created by solving stress constrained topology optimization model established by independent continuous mapping (ICM) method. The pre-trained CR-Pix2pix NN is capable of accurately predicting the high-resolution optimized configuration by inputting the low-resolution IC with the only one iteration step. Furthermore, the hyperparameters of the developed model are discussed to ensure the performance in accuracy and computational efficiency. The proposed method can be generalized to other precise topology optimization scenarios and engineering design.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 11872080), Beijing Natural Science Foundation (Grant No. 3192005).

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Authors and Affiliations

  1. Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Jicheng Li, Hongling Ye, Boshuai Yuan & Nan Wei

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  1. Jicheng Li
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  2. Hongling Ye
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  3. Boshuai Yuan
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  4. Nan Wei
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Correspondence to Hongling Ye.

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Li, J., Ye, H., Yuan, B. et al. Cross-resolution topology optimization for geometrical non-linearity by using deep learning. Struct Multidisc Optim 65, 133 (2022). https://doi.org/10.1007/s00158-022-03231-y

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  • DOI: https://doi.org/10.1007/s00158-022-03231-y

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