REFERENCES
1. Bischoff, M.; Bletzinger, K. U.; Wall, W. A.; Ramm, E. Models and finite elements for thin-walled structures. In: Stein E, Borst R, Hughes TJR, editors. Encyclopedia of Computational Mechanics. Wiley; 2004.
2. Loughlan, J. Thin-walled structures: advances in research, design and manufacturing technology. 1st edition. CRC Press; 2018.
3. Chen, D.; Gao, K.; Yang, J.; Zhang, L. Functionally graded porous structures: analyses, performances, and applications - a review. Thin. Walled. Struct. 2023, 191, 111046.
4. Ren, R.; Ma, X.; Yue, H.; Yang, F.; Lu, Y. Stiffness enhancement methods for thin-walled aircraft structures: a review. Thin. Walled. Struct. 2024, 201, 111995.
5. Azanaw, G. M. Thin-walled structures in structural engineering: a comprehensive review of design innovations, stability challenges, and sustainable frontiers. Am. J. Mater. Synth. Process. 2025, 10, 18-26.
6. Wan, X.; Xiao, Z.; Tian, Y.; et al. Recent advances in 4D printing of advanced materials and structures for functional applications. Adv. Mater. 2024, 36, e2312263.
7. Yarali, E.; Mirzaali, M. J.; Ghalayaniesfahani, A.; Accardo, A.; Diaz-Payno, P. J.; Zadpoor, A. A. 4D printing for biomedical applications. Adv. Mater. 2024, 36, e2402301.
8. Kuang, X.; Roach, D. J.; Wu, J.; et al. Advances in 4D printing: materials and applications. Adv. Funct. Mater. 2019, 29, 1805290.
9. Khoo, Z. X.; Teoh, J. E. M.; Liu, Y.; et al. 3D printing of smart materials: a review on recent progresses in 4D printing. Virt. Phys. Prototyping. 2015, 10, 103-22.
10. Jin, B.; Liu, J.; Shi, Y.; Chen, G.; Zhao, Q.; Yang, S. Solvent-assisted 4D programming and reprogramming of liquid crystalline organogels. Adv. Mater. 2022, 34, e2107855.
11. Yang, Y.; Wang, Y.; Lin, M.; Liu, M.; Huang, C. Bio-inspired facile strategy for programmable osmosis-driven shape-morphing elastomer composite structures. Mater. Horiz. 2024, 11, 2180-90.
12. Khalid, M. Y.; Otabil, A.; Mamoun, O. S.; Askar, K.; Bodaghi, M. Transformative 4D printed SMPs into soft electronics and adaptive structures: innovations and practical insights. Adv. Mater. Technol. 2025, 10, e00309.
13. Liu, B.; Li, H.; Meng, F.; et al. 4D printed hydrogel scaffold with swelling-stiffening properties and programmable deformation for minimally invasive implantation. Nat. Commun. 2024, 15, 1587.
14. Zhang, L.; Huang, X.; Cole, T.; et al. 3D-printed liquid metal polymer composites as NIR-responsive 4D printing soft robot. Nat. Commun. 2023, 14, 7815.
15. Soleimanzadeh, H.; Bodaghi, M.; Jamalabadi, M.; Rolfe, B.; Zolfagharian, A. Rotary 4D printing of programmable metamaterials on sustainable 4D mandrel. Adv. Mater. Technol. 2025, e01581.
16. Yan, Z.; Zhang, F.; Wang, J.; et al. Controlled mechanical buckling for origami-inspired construction of 3D microstructures in advanced materials. Adv. Funct. Mater. 2016, 26, 2629-39.
17. Yang, X.; Liu, M.; Zhang, B.; et al. Hierarchical tessellation enables programmable morphing matter. Matter 2024, 7, 603-19.
18. Nojoomi, A.; Arslan, H.; Lee, K.; Yum, K. Bioinspired 3D structures with programmable morphologies and motions. Nat. Commun. 2018, 9, 3705.
19. Arslan, H.; Nojoomi, A.; Jeon, J.; Yum, K. 3D printing of anisotropic hydrogels with bioinspired motion. Adv. Sci. 2019, 6, 1800703.
20. Boley, J. W.; van Ree, W. M.; Lissandrello, C.; et al. Shape-shifting structured lattices via multimaterial 4D printing. Proc. Natl. Acad. Sci. U. S. A. 2019, 116, 20856-62.
21. Sun, X.; Yue, L.; Yu, L.; et al. Machine learning-enabled forward prediction and inverse design of 4D-printed active plates. Nat. Commun. 2024, 15, 5509.
22. Chiu, Y. H.; Liao, Y. H.; Juang, J. Y. Designing bioinspired composite structures via genetic algorithm and conditional variational autoencoder. Polymers 2023, 15, 281.
23. Jin, L.; Yu, S.; Cheng, J.; et al. Machine learning driven forward prediction and inverse design for 4D printed hierarchical architecture with arbitrary shapes. Appl. Mater. Today. 2024, 40, 102373.
24. Sun, X.; Yue, L.; Yu, L.; et al. Machine learning-evolutionary algorithm enabled design for 4D-printed active composite structures. Adv. Funct. Mater. 2022, 32, 2109805.
25. Athinarayanarao, D.; Prod’hon, R.; Chamoret, D.; et al. Computational design for 4D printing of topology optimized multi-material active composites. npj. Comput. Mater. 2023, 9, 962.
26. Bai, Y.; Wang, H.; Xue, Y.; et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 2022, 609, 701-8.
27. Yang, X.; Zhou, Y.; Zhao, H.; et al. Morphing matter: from mechanical principles to robotic applications. Soft. Sci. 2023, 3, 38.
28. Ni, X.; Luan, H.; Kim, J. T.; et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nat. Commun. 2022, 13, 5576.
29. Wang, X.; Guo, X.; Ye, J.; et al. Freestanding 3D mesostructures, functional devices, and shape-programmable systems based on mechanically induced assembly with shape memory polymers. Adv. Mater. 2019, 31, e1805615.
30. Tong, D.; Hao, Z.; Liu, M.; Huang, W. Inverse design of snap-actuated jumping robots powered by mechanics-aided machine learning. IEEE. Robot. Autom. Lett. 2025, 10, 1720-7.
31. Bodaghi, M.; Namvar, N.; Yousefi, A.; Teymouri, H.; Demoly, F.; Zolfagharian, A. Metamaterial boat fenders with supreme shape recovery and energy absorption/dissipation via FFF 4D printing. Smart. Mater. Struct. 2023, 32, 095028.
32. Jolaiy, S.; Yousefi, A.; Hosseini, M.; Zolfagharian, A.; Demoly, F.; Bodaghi, M. Limpet-inspired design and 3D/4D printing of sustainable sandwich panels: pioneering supreme resiliency, recoverability and repairability. Appl. Mater. Today. 2024, 38, 102243.
33. Zolfagharian, A.; Jarrah, H. R.; Xavier, M. S.; Rolfe, B.; Bodaghi, M. Multimaterial 4D printing with a tunable bending model. Smart. Mater. Struct. 2023, 32, 065001.
34. Nojoomi, A.; Jeon, J.; Yum, K. 2D material programming for 3D shaping. Nat. Commun. 2021, 12, 603.
35. Ahn, S. J.; Byun, J.; Joo, H. J.; Jeong, J. H.; Lee, D. Y.; Cho, K. J. 4D printing of continuous shape representation. Adv. Mater. Technol. 2021, 6, 2100133.
36. Kansara, H.; Liu, M.; He, Y.; Tan, W. Inverse design and additive manufacturing of shape-morphing structures based on functionally graded composites. J. Mech. Phys. Solids. 2023, 180, 105382.
37. Yun, S.; Ahn, Y.; Kim, S. Tailoring elastomeric meshes with desired 1D tensile behavior using an inverse design algorithm and material extrusion printing. Addit. Manuf. 2022, 60, 103254.
38. Zheng, X.; Zhang, X.; Chen, T. T.; Watanabe, I. Deep learning in mechanical metamaterials: from prediction and generation to inverse design. Adv. Mater. 2023, 35, e2302530.
39. Chen, C. T.; Gu, G. X. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv. Sci. 2020, 7, 1902607.
40. Chen, C. T.; Gu, G. X. Physics-informed deep-learning for elasticity: forward, inverse, and mixed problems. Adv. Sci. 2023, 10, e2300439.
41. Wang, R.; Peng, X.; Wang, X.; Wu, C.; Liang, X.; Wu, W. DM net: a multiple nonlinear regression net for the inverse design of disordered metamaterials. Addit. Manuf. 2024, 96, 104577.
42. Mohammadi, M.; Kouzani, A. Z.; Bodaghi, M.; et al. Sustainable robotic joints 4D printing with variable stiffness using reinforcement learning. Robotics. Comput. Integr. Manuf. 2024, 85, 102636.
43. Mohammadi, M.; Kouzani, A. Z.; Bodaghi, M.; Zolfagharian, A. Inverse design of adaptive flexible structures using physical-enhanced neural network. Virtual. Phys. Prototyp. 2025, 20, e2530732.
44. Cheng, X.; Fan, Z.; Yao, S.; et al. Programming 3D curved mesosurfaces using microlattice designs. Science 2023, 379, 1225-32.
45. Chiu, Y.; Huang, Y.; Chen, M.; Xu, Y.; Yen, Y.; Juang, J. Inverse design of face-like 3D surfaces via bi-material 4D printing and shape morphing. Virt. Phys. Prototyping. 2025, 20, e2507099.
46. Bodaghi, M.; Damanpack, A. R.; Liao, W. H. Triple shape memory polymers by 4D printing. Smart. Mater. Struct. 2018, 27, 065010.
47. Ma, D. S.; Correll, J.; Wittenbrink, B. The Chicago face database: a free stimulus set of faces and norming data. Behav. Res. Methods. 2015, 47, 1122-35.
48. Ma, D. S.; Kantner, J.; Wittenbrink, B. Chicago face database: multiracial expansion. Behav. Res. Methods. 2021, 53, 1289-300.
49. Lakshmi, A.; Wittenbrink, B.; Correll, J.; Ma, D. S. The India face set: international and cultural boundaries impact face impressions and perceptions of category membership. Front. Psychol. 2021, 12, 627678.
50. Thomaz, C. E.; Giraldi, G. A. A new ranking method for principal components analysis and its application to face image analysis. Image. Vis. Comput. 2010, 28, 902-13.
51. DeBruine, L.; Jones, B. Face research lab london set. 2021. https://figshare.com/articles/dataset/Face_Research_Lab_London_Set/5047666. (accessed 2025-11-11).
53. Ma, L.; Tan, T.; Wang, Y.; Zhang, D. Efficient iris recognition by characterizing key local variations. IEEE. Trans. Image. Process. 2004, 13, 739-50.
54. Bashkatov, A. N.; Koblova, E. V.; Bakutkin, V. V.; Genina, E. A.; Savchenko, E. P.; Tuchin, V. V. Estimation of melanin content in iris of human eye. In Ophthalmic Technologies XV. SPIE; 2005. pp. 302-11.
55. Iyamu, E.; Osuobeni, E. Age, gender, corneal diameter, corneal curvature and central corneal thickness in Nigerians with normal intra ocular pressure. J. Optomm. 2012, 5, 87-97.
57. Mohammed, S.; Budach, L.; Feuerpfeil, M.; et al. The effects of data quality on machine learning performance on tabular data. Inf. Syst. 2025, 132, 102549.
58. Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention is all you need. arXiv 2017, arXiv:1706.03762. Available online: https://doi.org/10.48550/arXiv.1706.03762. (accessed 11 Nov 2025).
59. Lin, K.; Zhao, Y.; Zhou, T.; et al. Applying machine learning to fine classify construction and demolition waste based on deep residual network and knowledge transfer. Environ. Dev. Sustain. 2023, 25, 8819-36.
61. Mitteroecker, P.; Gunz, P.; Windhager, S.; Schaefer, K. A brief review of shape, form, and allometry in geometric morphometrics, with applications to human facial morphology. Hystrix 2013, 24, 59-66.
62. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA. June 27-30, 2016. IEEE; 2016. pp. 770-8.
63. Chen, Z.; Badrinarayanan, V.; Lee, C. Y.; Rabinovich, A. GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. arXiv 2017, arXiv:1711.02257. Available online: https://doi.org/10.48550/arXiv.1711.02257. (accessed 11 Nov 2025).
64. Wang, Z.; Bovik, A. C.; Sheikh, H. R.; Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE. Trans. Image. Process. 2004, 13, 600-12.
65. Yoo, J.; Han, T. H. Fast normalized cross-correlation. Circuits. Syst. Signal. Process. 2009, 28, 819-43.
66. Radford, A.; Metz, L.; Chintala, S. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015, arXiv:1511.06434. Available online: https://doi.org/10.48550/arXiv.1511.06434. (accessed 11 Nov 2025).
67. Kaelbling, L. P.; Littman, M. L.; Moore, A. W. Reinforcement learning: a survey. arXiv 1996, arXiv:cs/9605103. Available online: https://doi.org/10.48550/arXiv.cs/9605103. (accessed 11 Nov 2025).
68. Wiering, M.; Otterlo, M. Reinforcement learning: state-of-art. 1st edition. Springer Berlin, 2012.
69. Li, Y. Deep reinforcement learning: an overview. arXiv 2017, arXiv:1701.07274. Available online: https://doi.org/10.48550/arXiv.1701.07274. (accessed 11 Nov 2025).
70. Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014, 15, 1929-58. https://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf?utm_content=buffer79b4. (accessed 11 Nov 2025).
71. Aung, S. C.; Foo, C. L.; Lee, S. T. Three dimensional laser scan assessment of the Oriental nose with a new classification of Oriental nasal types. Br. J. Plast. Surg. 2000, 53, 109-16.
72. Uzun, A.; Akbas, H.; Bilgic, S.; et al. The average values of the nasal anthropometric measurements in 108 young Turkish males. Auris. Nasus. Larynx. 2006, 33, 31-5.
73. Farkas, L. G.; Katic, M. J.; Forrest, C. R.; et al. International anthropometric study of facial morphology in various ethnic groups/races. J. Craniofac. Surg. 2005, 16, 615-46.
74. Chen, Y.; Medioni, G. Object modelling by registration of multiple range images. Image. Vis. Comput. 1992, 10, 145-55.
