journal article·2025-12

Generative adversarial network for stress-minimizing topology optimization

Xiaomei Huang, Shijiang Li, Congmin Miao, Liang Hou, Yun Chen

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OpenAlex topics: Topology Optimization in Engineering · Advanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research

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References (40)

Show source-provided references
1. Almasri, W., Bettebghor, D., Adjed, F., Danglade, F., Ababsa, F.: Geometrically-driven generation of mechanical designs through deep convolutional GANs. Eng. Optim. 56, 18–35 (2024). https://doi.org/10.1080/0305215X.2022.214484710.1080/0305215X.2022.2144847 ↗
2. Chen, X., Yao, W., Zhou, W., Zhang, Z., Li, Y.: A general differentiable layout optimization framework for heat transfer problems. Int. J. Heat Mass Transf. 211, 124205 (2023). https://doi.org/10.1016/j.ijheatmasstransfer.2023.12420510.1016/j.ijheatmasstransfer.2023.124205 ↗
3. Chen, X., Zhao, X., Gong, Z., Zhang, J., Zhou, W., Chen, X., Yao, W.: A deep neural network surrogate modeling benchmark for temperature field prediction of heat source layout. Sci. China Phys. Mech. Astron. 64, 1 (2021). https://doi.org/10.1007/s11433-021-1755-610.1007/s11433-021-1755-6 ↗
4. Cheng, G., Jiang, Z.: Study on topology optimization with stress constraints. Eng. Optim. 20, 129–148 (1992). https://doi.org/10.1080/0305215920894127610.1080/03052159208941276 ↗
5. Deng, H., To, A.C.: Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design. Comput. Mech. 66, 449–469 (2020). https://doi.org/10.1007/s00466-020-01859-510.1007/s00466-020-01859-5 ↗
6. Guo, X., Zhang, W., Zhang, J., Yuan, J.: Explicit structural topology optimization based on moving morphable components (MMC) with curved skeletons. Comput. Methods Appl. Mech. Eng. 310, 711–748 (2016). https://doi.org/10.1016/j.cma.2016.07.01810.1016/j.cma.2016.07.018 ↗
7. Hamdia, K.M., Ghasemi, H., Bazi, Y., AlHichri, H., Alajlan, N., Rabczuk, T.: A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization. Finite Elements Anal. Design. 165, 21–30 (2019a). https://doi.org/10.1016/j.finel.2019.07.00110.1016/j.finel.2019.07.001 ↗
8. Hamdia, K.M., Ghasemi, H., Zhuang, X., Alajlan, N., Rabczuk, T.: Computational machine learning representation for the flexoelectricity effect in truncated pyramid structures. Comput. Mater. Contin. 59, 79–87 (2019b). https://doi.org/10.32604/cmc.2019.0588210.32604/cmc.2019.05882 ↗
9. Hertlein, N., Buskohl, P.R., Gillman, A., Vemaganti, K., Anand, S.: Generative adversarial network for early-stage design flexibility in topology optimization for additive manufacturing. J. Manuf. Syst. 59, 675–685 (2021). https://doi.org/10.1016/j.jmsy.2021.04.00710.1016/j.jmsy.2021.04.007 ↗
10. Hu, J., Liu, Y., Luo, Y., Huang, H., Liu, S.: Topology optimization of multi-material structures considering a piecewise interface stress constraint. Comput. Methods Appl. Mech. Eng. 398, 115274 (2022). https://doi.org/10.1016/j.cma.2022.11527410.1016/j.cma.2022.115274 ↗
11. Huang, X., Chen, Y., Hou, L., Miao, C., Li, Y.: Stress-related multi-material structures topology optimization with gradient interfaces. Compos. Struct. 365, 119176 (2025a). https://doi.org/10.1016/j.compstruct.2025.11917610.1016/j.compstruct.2025.119176 ↗
12. Huang, X., Chen, Y., Hou, L., Wu, X., Yan, H., Li, Y., Yang, M., Zhong, Z.: Multi-material topology optimisation with design-dependent loads using the ordered EAMP quadratic penalty method. J. Eng. Des. 1–28 (2025b). https://doi.org/10.1080/09544828.2025.250485110.1080/09544828.2025.2504851 ↗
13. Huang, X., Xie, Y.M.: Convergent and mesh-independent solutions for the bi-directional evolutionary structural optimization method. Finite Elem. Anal. des. 43, 1039–1049 (2007). https://doi.org/10.1016/j.finel.2007.06.00610.1016/j.finel.2007.06.006 ↗
14. Jeong, H., Bai, J., Batuwatta-Gamage, C.P., Rathnayaka, C., Zhou, Y., Gu, Y.: A physics-informed neural network-based topology optimization (PINNTO) framework for structural optimization. Eng. Struct. 278, 115484 (2023a). https://doi.org/10.1016/j.engstruct.2022.11548410.1016/j.engstruct.2022.115484 ↗
15. Jeong, H., Batuwatta-Gamage, C., Bai, J., Rathnayaka, C., Zhou, Y., Gu, Y.: An advanced physics-informed neural network-based framework for nonlinear and complex topology optimization. Eng. Struct. 322, 119194 (2025). https://doi.org/10.1016/j.engstruct.2024.11919410.1016/j.engstruct.2024.119194 ↗
16. Jeong, H., Batuwatta-Gamage, C., Bai, J., Xie, Y.M., Rathnayaka, C., Zhou, Y., Gu, Y.: A complete Physics-Informed Neural Network-based framework for structural topology optimization. Comput. Methods Appl. Mech. Eng. 417, 116401 (2023b). https://doi.org/10.1016/j.cma.2023.11640110.1016/j.cma.2023.116401 ↗
17. Li, J., Ye, H., Wei, N., Dong, Y.: Efficient multi-material topology optimization design with minimum compliance based on ResUNet involved generative adversarial network. Acta Mech. Sin. 40, 423185 (2024). https://doi.org/10.1007/s10409-023-23185-x10.1007/s10409-023-23185-x ↗
18. Liao, H., Ding, W., Ai, S., Gao, R.: A single variable stress-based multi-material topology optimization method with three-dimensional unstructured meshes. Comput. Methods Appl. Mech. Eng. 421, 116774 (2024). https://doi.org/10.1016/j.cma.2024.11677410.1016/j.cma.2024.116774 ↗
19. Liu, B., Wang, Y., Rabczuk, T., Olofsson, T., Lu, W.: Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks. Renew. Energy 220, 119565 (2024). https://doi.org/10.1016/j.renene.2023.11956510.1016/j.renene.2023.119565 ↗
20. Liu, L., Yan, J., Cheng, G.: Optimum structure with homogeneous optimum truss-like material. Comput. Struct. 86, 1417–1425 (2008). https://doi.org/10.1016/j.compstruc.2007.04.03010.1016/j.compstruc.2007.04.030 ↗
21. Nie, Z., Jiang, H., Lin, T., Kara, L.B.: TopologyGAN: topology optimization using generative adversarial networks based on physical fields over the initial domain. J. Mech. des. 143, 031715 (2021). https://doi.org/10.1115/1.404953310.1115/1.4049533 ↗
22. París, J., Navarrina, F., Colominas, I., Casteleiro, M.: Topology optimization of continuum structures with local and global stress constraints. Struct. Multidiscip. Optim. 39, 419–437 (2009). https://doi.org/10.1007/s00158-008-0336-210.1007/s00158-008-0336-2 ↗
23. Sugai, T., Shintani, K., Yamada, T.: Data-driven topology optimization using a multitask conditional variational autoencoder with persistent homology. Struct. Multidiscip. Optim. 67, 133 (2024). https://doi.org/10.1007/s00158-024-03851-610.1007/s00158-024-03851-6 ↗
24. Sun, Z., Liu, W., Liu, P., Luo, Y.: A new multiscale concurrent topology optimization method based on the ordered interpolation and MFSE model. Comput. Methods Appl. Mech. Eng. 430, 117230 (2024). https://doi.org/10.1016/j.cma.2024.11723010.1016/j.cma.2024.117230 ↗
25. Wang, D., Ning, Y., Xiang, C., Chen, A.: A two-stage network framework for topology optimization incorporating deep learning and physical information. Eng. Appl. Artif. Intell. 133, 108185 (2024). https://doi.org/10.1016/j.engappai.2024.10818510.1016/j.engappai.2024.108185 ↗
26. Wang, D., Xiang, C., Pan, Y., Chen, A., Zhou, X., Zhang, Y.: A deep convolutional neural network for topology optimization with perceptible generalization ability. Eng. Optim. 54, 973–988 (2022a). https://doi.org/10.1080/0305215X.2021.190299810.1080/0305215X.2021.1902998 ↗
27. Wang, L., Shi, D., Zhang, B., Li, G., Liu, P.: Real-time topology optimization based on deep learning for moving morphable components. Autom. Constr. 142, 104492 (2022b). https://doi.org/10.1016/j.autcon.2022.10449210.1016/j.autcon.2022.104492 ↗
28. Wang, M.Y., Wang, X., Guo, D.: A level set method for structural topology optimization. Comput. Methods Appl. Mech. Eng. 192, 227–246 (2003). https://doi.org/10.1016/S0045-7825(02)00559-510.1016/S0045-7825(02)00559-5 ↗
29. Wu, Y., Wang, L., Li, Z., Wu, L., Liu, Y.: A deep generative multiscale topology optimization framework considering manufacturing defects and parametrical uncertainties. Comput. Methods Appl. Mech. Eng. 437, 117778 (2025). https://doi.org/10.1016/j.cma.2025.11777810.1016/j.cma.2025.117778 ↗
30. Xia, Y., Zhang, C., Wang, C., Liu, H., Sang, X., Liu, R., Zhao, P., An, G., Fang, H., Shi, M., Li, B., Yuan, Y., Liu, B.: Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning. Tunnell. Undergr. Space Technol. 140, 105319 (2023a). https://doi.org/10.1016/j.tust.2023.10531910.1016/j.tust.2023.105319 ↗
31. Xia, Z., Wang, J., Li, Y., Zhang, L., Liu, C.: Intelligent design of key joints in aerial building machine using topology optimization and generative adversarial network. Autom. Constr. 168, 105747 (2024). https://doi.org/10.1016/j.autcon.2024.10574710.1016/j.autcon.2024.105747 ↗
32. Xia, Z., Zhang, H., Zhuang, Z., Yu, C., Yu, J., Gao, L.: A machine-learning framework for isogeometric topology optimization. Struct. Multidiscip. Optim. 66, 83 (2023b). https://doi.org/10.1007/s00158-023-03539-310.1007/s00158-023-03539-3 ↗
33. Xiang, C., Chen, A., Wang, D.: Real-time stress-based topology optimization via deep learning. Thin-Walled Structures 181, 110055 (2022). https://doi.org/10.1016/j.tws.2022.11005510.1016/j.tws.2022.110055 ↗
34. Xing, Y., Tong, L.: Approximated and neural network assisted quadratic programming and its applications in structural topology optimization, https://www.ssrn.com/abstract=4977867, (2024)10.2139/ssrn.4977867 ↗
35. Xing, Y., Tong, L.: A stochastic gradient online learning and prediction method for accelerating structural topology optimization using recurrent neural network. Eng. Struct. 338, 120507 (2025). https://doi.org/10.1016/j.engstruct.2025.12050710.1016/j.engstruct.2025.120507 ↗
36. Xu, S., Liu, J., Zou, B., Li, Q., Ma, Y.: Stress constrained multi-material topology optimization with the ordered SIMP method. Comput. Methods Appl. Mech. Eng. 373, 113453 (2021). https://doi.org/10.1016/j.cma.2020.11345310.1016/j.cma.2020.113453 ↗
37. Yan, J., Zhang, Q., Xu, Q., Fan, Z., Li, H., Sun, W., Wang, G.: Deep learning driven real time topology optimisation based on initial stress learning. Adv. Eng. Inform. 51, 101472 (2022). https://doi.org/10.1016/j.aei.2021.10147210.1016/j.aei.2021.101472 ↗
38. Zhang, Z., Li, Y., Zhou, W., Chen, X., Yao, W., Zhao, Y.: TONR: an exploration for a novel way combining neural network with topology optimization. Comput. Methods Appl. Mech. Eng. 386, 114083 (2021). https://doi.org/10.1016/j.cma.2021.11408310.1016/j.cma.2021.114083 ↗
39. Zhang, Z., Yao, W., Li, Y., Zhou, W., Chen, X.: Topology optimization via implicit neural representations. Comput. Methods Appl. Mech. Eng. 411, 116052 (2023). https://doi.org/10.1016/j.cma.2023.11605210.1016/j.cma.2023.116052 ↗
40. Zhao, X., Mezzadri, F., Wang, T., Qian, X.: Physics-informed neural network based topology optimization through continuous adjoint. Struct. Multidiscip. Optim. 67, 143 (2024). https://doi.org/10.1007/s00158-024-03856-110.1007/s00158-024-03856-1 ↗