{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T02:49:12Z","timestamp":1777949352862,"version":"3.51.4"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Major Science and Technology Projects","award":["No.318J009"],"award-info":[{"award-number":["No.318J009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-44111-5","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:44:20Z","timestamp":1773834260000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative adversarial networks for high-fidelity 3D point cloud completion"],"prefix":"10.1038","volume":"16","author":[{"given":"Di","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sizhe","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhan","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"44111_CR1","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.aei.2019.02.007","volume":"39","author":"Q Wang","year":"2019","unstructured":"Wang, Q. & Kim, M. K. Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018. Adv. Eng. Inform. 39, 306\u2013319. https:\/\/doi.org\/10.1016\/j.aei.2019.02.007 (2019).","journal-title":"Adv. Eng. Inform."},{"key":"44111_CR2","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1007\/s10586-025-05727-w","volume":"28","author":"A Chauhan","year":"2025","unstructured":"Chauhan, A. et al. A survey of deep reinforcement learning techniques for energy-efficient green cloud computing. Cluster Comput. 28, 989. https:\/\/doi.org\/10.1007\/s10586-025-05727-w (2025).","journal-title":"Cluster Comput."},{"key":"44111_CR3","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/s00530-025-01887-2","volume":"31","author":"Z Yang","year":"2025","unstructured":"Yang, Z. et al. Mpv-pcqa: multimodal no-reference point cloud quality assessment via point cloud and captured dynamic video. Multimedia Syst. 31, 310. https:\/\/doi.org\/10.1007\/s00530-025-01887-2 (2025).","journal-title":"Multimedia Syst."},{"key":"44111_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-025-03918-5","author":"T Xu","year":"2025","unstructured":"Xu, T. et al. A miniature A-mode ultrasound system for noninvasive bone surface point cloud acquisition. Ann. Biomed. Eng. https:\/\/doi.org\/10.1007\/s10439-025-03918-5 (2025).","journal-title":"Ann. Biomed. Eng."},{"key":"44111_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-95-2101-2_32","volume-title":"Intelligent robotics and applications. ICIRA 2025","author":"S Zhang","year":"2026","unstructured":"Zhang, S., Hu, S., Zhao, X., Zhang, D. & Tao, B. An accurate 3D reconstruction method for large workpieces based on 3D vision. In: (eds Matsuno, T. et al.) Intelligent robotics and applications. ICIRA 2025. Lecture Notes in Computer Science, vol. 16076. Springer, Singapore https:\/\/doi.org\/10.1007\/978-981-95-2101-2_32. (2026)."},{"issue":"2","key":"44111_CR6","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/s11390-024-3419-7","volume":"39","author":"Y Jung","year":"2024","unstructured":"Jung, Y. et al. A transfer function design for medical volume data using a knowledge database based on deep image and primitive intensity profile features retrieval. J. Comput. Sci. Technol. 39 (2), 320\u2013335. https:\/\/doi.org\/10.1007\/s11390-024-3419-7 (2024).","journal-title":"J. Comput. Sci. Technol."},{"key":"44111_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3572584","author":"J Wang","year":"2025","unstructured":"Wang, J. et al. Non-rigid point cloud registration via anisotropic hybrid field harmonization. IEEE Trans. Pattern Anal. Mach. Intell. https:\/\/doi.org\/10.1109\/TPAMI.2025.3572584 (2025).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"44111_CR8","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Luo, Y. & Sun, T. A quantitative 3D reconstruction evaluation method based on Blender. In: Proceedings of the 10th International Conference on Computer and Communications (ICCC), 761\u2013765 (2024). https:\/\/doi.org\/10.1109\/ICCC62609.2024.10942306","DOI":"10.1109\/ICCC62609.2024.10942306"},{"key":"44111_CR9","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s00371-024-03354-x","volume":"41","author":"L Wang","year":"2025","unstructured":"Wang, L. et al. A cascaded graph convolutional network for point cloud completion. Vis. Comput. 41, 659\u2013674. https:\/\/doi.org\/10.1007\/s00371-024-03354-x (2025).","journal-title":"Vis. Comput."},{"key":"44111_CR10","doi-asserted-by":"publisher","unstructured":"Lu, C. H. & Chen, X. H. Improved iterative Poisson point cloud surface reconstruction. In: Proceedings of the 3rd International Conference on Digital Society and Intelligent Systems (DSInS), 382\u2013385 (2023). https:\/\/doi.org\/10.1109\/DSInS60115.2023.10455616","DOI":"10.1109\/DSInS60115.2023.10455616"},{"key":"44111_CR11","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s00371-025-04295-9","volume":"42","author":"M Li","year":"2026","unstructured":"Li, M., Li, G. & Li, X. PoseNorm-PCN: pose-normalized human point cloud completion from a single front view. Vis. Comput. 42, 52. https:\/\/doi.org\/10.1007\/s00371-025-04295-9 (2026).","journal-title":"Vis. Comput."},{"key":"44111_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73247-8_8","volume-title":"Computer vision\u2014ECCV 2024. ECCV 2024","author":"Z Fu","year":"2025","unstructured":"Fu, Z. et al. AEDNet: adaptive embedding and multiview-aware disentanglement for point cloud completion. In: (eds Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G.) Computer Vision\u2014ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol. 15069. Springer, Cham https:\/\/doi.org\/10.1007\/978-3-031-73247-8_8. (2025)."},{"key":"44111_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, M. et al. Joint-learning: a robust segmentation method for 3D point clouds under label noise. Computer Animation and Virtual Worlds 36.3, e70038 (2025). https:\/\/doi.org\/10.1002\/cav.70038","DOI":"10.1002\/cav.70038"},{"key":"44111_CR14","doi-asserted-by":"publisher","first-page":"3903","DOI":"10.1007\/s00371-024-03396-1","volume":"40","author":"J Ji","year":"2024","unstructured":"Ji, J., Zhao, R. & Lei, M. Latent diffusion transformer for point cloud generation. Vis. Comput. 40, 3903\u20133917. https:\/\/doi.org\/10.1007\/s00371-024-03396-1 (2024).","journal-title":"Vis. Comput."},{"key":"44111_CR15","doi-asserted-by":"publisher","first-page":"8287","DOI":"10.1007\/s00371-023-03237-7","volume":"40","author":"KA Tychola","year":"2024","unstructured":"Tychola, K. A., Vrochidou, E. & Papakostas, G. A. Deep learning based computer vision under the prism of 3D point clouds: a systematic review. Vis. Comput. 40, 8287\u20138329. https:\/\/doi.org\/10.1007\/s00371-023-03237-7 (2024).","journal-title":"Vis. Comput."},{"key":"44111_CR16","doi-asserted-by":"publisher","first-page":"12441","DOI":"10.1007\/s00371-025-04165-4","volume":"41","author":"G Yao","year":"2025","unstructured":"Yao, G. et al. DS-GAN: a dual sub-structure GAN for thermal infrared image colorization using U-Net with ConvNeXt and multi-scale large kernel attention. Vis. Comput. 41, 12441\u201312459. https:\/\/doi.org\/10.1007\/s00371-025-04165-4 (2025).","journal-title":"Vis. Comput."},{"key":"44111_CR17","doi-asserted-by":"publisher","first-page":"5334","DOI":"10.1109\/TVCG.2024.3447351","volume":"31.9","author":"X Hu","year":"2024","unstructured":"Hu, X. et al. Msembgan: Multi-stitch embroidery synthesis via region-aware texture generation. IEEE Trans. Vis. Comput. Graph. 31.9, 5334\u20135347. https:\/\/doi.org\/10.1109\/TVCG.2024.3447351 (2024).","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"44111_CR18","doi-asserted-by":"publisher","unstructured":"Chen, Z., Hu, Z., Dai, S. & Zhou, L. KANs vs MLPs in OT-GAN. In: Proceedings of the 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), pp. 356\u2013360 (2024). https:\/\/doi.org\/10.1109\/ISCEIC63613.2024.10810254","DOI":"10.1109\/ISCEIC63613.2024.10810254"},{"key":"44111_CR19","unstructured":"Kazhdan, M., Bolitho, M. & Hoppe, H. Poisson surface reconstruction. In: Proceedings of the 4th Eurographics Symposium on Geometry Processing (SGP), pp. 61\u201370 (2006)."},{"key":"44111_CR20","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s41095-022-0315-3","volume":"10","author":"B Leng","year":"2024","unstructured":"Leng, B. et al. Shape embedding and retrieval in multi-flow deformation. Comput. Vis. Media. 10, 439\u2013451. https:\/\/doi.org\/10.1007\/s41095-022-0315-3 (2024).","journal-title":"Comput. Vis. Media"},{"key":"44111_CR21","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1109\/JSTARS.2022.3229062","volume":"16","author":"G Liang","year":"2023","unstructured":"Liang, G., Zhao, X., Zhao, J. & Zhou, F. MVCNN: a deep learning-based ocean\u2013land waveform classification network for single-wavelength LiDAR bathymetry. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 656\u2013674. https:\/\/doi.org\/10.1109\/JSTARS.2022.3229062 (2023).","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"44111_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_38","volume-title":"Medical image computing and computer assisted intervention\u2014MICCAI 2021. MICCAI 2021","author":"M Toikkanen","year":"2021","unstructured":"Toikkanen, M., Kwon, D. & Lee, M. ReSGAN: intracranial hemorrhage segmentation with residuals of synthetic brain CT scans. In: (eds de Bruijne, M. et al.) Medical image computing and computer assisted intervention\u2014MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol. 12901. Springer, Cham https:\/\/doi.org\/10.1007\/978-3-030-87193-2_38. (2021)."},{"key":"44111_CR23","doi-asserted-by":"publisher","unstructured":"Dai, A., Qi, C. R. & Nie\u00dfner, M. Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6545\u20136554 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.693","DOI":"10.1109\/CVPR.2017.693"},{"key":"44111_CR24","doi-asserted-by":"publisher","first-page":"60025","DOI":"10.1109\/ACCESS.2022.3180339","volume":"10","author":"YH Chung","year":"2022","unstructured":"Chung, Y. H. & Chen, Y. L. Three-dimensional image inpainting system using 3D-ED-GAN for efficient vision-based detection for rotor dynamic balance system. IEEE Access 10, 60025\u201360038. https:\/\/doi.org\/10.1109\/ACCESS.2022.3180339 (2022).","journal-title":"IEEE Access"},{"key":"44111_CR25","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s10343-025-01154-4","volume":"77","author":"L Xu","year":"2025","unstructured":"Xu, L. et al. SPDGrNet: A Lightweight and Efficient Image Classification Network for Zea mays Diseases. J. Crop Health. 77, 92. https:\/\/doi.org\/10.1007\/s10343-025-01154-4 (2025).","journal-title":"J. Crop Health"},{"key":"44111_CR26","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s00371-024-03364-9","volume":"41","author":"J Li","year":"2025","unstructured":"Li, J. et al. Edge-guided generative network with attention for point cloud completion. Vis. Comput. 41, 785\u2013798. https:\/\/doi.org\/10.1007\/s00371-024-03364-9 (2025).","journal-title":"Vis. Comput."},{"key":"44111_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s00371-025-04337-2","volume":"42","author":"Z Tian","year":"2026","unstructured":"Tian, Z. et al. Enhanced 3D shoeprint classification via multi-scale PointNet\u2009+\u2009+\u2009with attention mechanisms. Vis. Comput. 42, 120. https:\/\/doi.org\/10.1007\/s00371-025-04337-2 (2026).","journal-title":"Vis. Comput."},{"key":"44111_CR28","doi-asserted-by":"publisher","unstructured":"Zan, G., Wang, Y. & Gao, P. Improved DGCNN based on Transformer for point cloud segmentation. In: Lu, H., Cai, J. (eds) Artificial Intelligence and Robotics. ISAIR 2023. Communications in Computer and Information Science. Springer, Singapore (2024). (1998). https:\/\/doi.org\/10.1007\/978-981-99-9109-9_27","DOI":"10.1007\/978-981-99-9109-9_27"},{"key":"44111_CR29","doi-asserted-by":"publisher","unstructured":"Chen, J. H. & Hsu, C. C. PointCNN-Hand: 3D hand joints estimate by PointCNN from hand point cloud. In: Proceedings of the 2021 International Conference on System Science and Engineering (ICSSE), 458\u2013463 (2021). https:\/\/doi.org\/10.1109\/ICSSE52999.2021.9538459","DOI":"10.1109\/ICSSE52999.2021.9538459"},{"key":"44111_CR30","doi-asserted-by":"publisher","unstructured":"Ma, X., Yin, Q., Zhang, X. & Tang, L. FoldingNet-based geometry compression of point cloud with multi descriptions. In: Proceedings of the 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 1\u20136 (2022). https:\/\/doi.org\/10.1109\/ICMEW56448.2022.9859339","DOI":"10.1109\/ICMEW56448.2022.9859339"},{"key":"44111_CR31","doi-asserted-by":"publisher","unstructured":"Gong, B., Nie, Y., Lin, Y., Han, X. & Yu, Y. ME-PCN: point completion conditioned on mask emptiness. In: Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 12468\u201312477 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01226","DOI":"10.1109\/ICCV48922.2021.01226"},{"key":"44111_CR32","doi-asserted-by":"publisher","unstructured":"Wang, X. et al. TopNet: transformer-efficient occupancy prediction network for octree-structured point cloud geometry compression. In: Proceedings of the 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 27305\u201327314 (2025). https:\/\/doi.org\/10.1109\/CVPR52734.2025.02543","DOI":"10.1109\/CVPR52734.2025.02543"},{"key":"44111_CR33","doi-asserted-by":"publisher","unstructured":"Huang, Z., Yu, Y., Xu, J., Ni, F. & Le, X. PF-Net: point fractal network for 3D point cloud completion. In: Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7659\u20137667 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00768","DOI":"10.1109\/CVPR42600.2020.00768"},{"key":"44111_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2022.03.060","volume":"491","author":"J Li","year":"2022","unstructured":"Li, J., Guo, S., Meng, X., Lai, Z. & Han, S. DPG-Net: densely progressive-growing network for point cloud completion. Neurocomputing 491, 1\u201313. https:\/\/doi.org\/10.1016\/j.neucom.2022.03.060 (2022).","journal-title":"Neurocomputing"},{"key":"44111_CR35","unstructured":"Liu, M., Sheng, L., Yang, S., Shao, J. & Hu, S. M. Morphing and sampling network for dense point cloud completion. arXiv preprint arXiv:1912.00280 (2019)."},{"key":"44111_CR36","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s11263-022-01588-7","volume":"130","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Tan, D. J., Navab, N. & Tombari, F. SoftPool++: an encoder\u2013decoder network for point cloud completion. Int. J. Comput. Vis. 130, 1145\u20131164. https:\/\/doi.org\/10.1007\/s11263-022-01588-7 (2022).","journal-title":"Int. J. Comput. Vis."},{"key":"44111_CR37","doi-asserted-by":"publisher","unstructured":"Wen, X. et al. PMP-Net: point cloud completion by learning multi-step point moving paths. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7443\u20137452 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00736","DOI":"10.1109\/CVPR46437.2021.00736"},{"key":"44111_CR38","doi-asserted-by":"publisher","first-page":"5857","DOI":"10.1007\/s00371-023-03140-1","volume":"40","author":"Z Liu","year":"2024","unstructured":"Liu, Z. & Xue, R. Visual image encryption based on compressed sensing and Cycle-GAN. Vis. Comput. 40, 5857\u20135870. https:\/\/doi.org\/10.1007\/s00371-023-03140-1 (2024).","journal-title":"Vis. Comput."},{"issue":"1","key":"44111_CR39","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.vrih.2022.08.016","volume":"5","author":"B Shen","year":"2023","unstructured":"Shen, B. et al. Point cloud upsampling generative adversarial network based on residual multi-scale off-set attention. Virtual Real. Intell. Hardw. 5 (1), 81\u201391. https:\/\/doi.org\/10.1016\/j.vrih.2022.08.016 (2023).","journal-title":"Virtual Real. Intell. Hardw."},{"key":"44111_CR40","doi-asserted-by":"publisher","first-page":"8205","DOI":"10.1007\/s00371-023-03233-x","volume":"40","author":"NK Yadav","year":"2024","unstructured":"Yadav, N. K., Singh, S. K. & Dubey, S. R. ISA-GAN: inception-based self-attentive encoder\u2013decoder network for face synthesis using delineated facial images. Vis. Comput. 40, 8205\u20138225. https:\/\/doi.org\/10.1007\/s00371-023-03233-x (2024).","journal-title":"Vis. Comput."},{"key":"44111_CR41","doi-asserted-by":"publisher","first-page":"8947","DOI":"10.1007\/s00371-025-03908-7","volume":"41","author":"W Chen","year":"2025","unstructured":"Chen, W. et al. Stacked deep fusion GAN for enhanced text-to-image generation. Vis. Comput. 41, 8947\u20138960. https:\/\/doi.org\/10.1007\/s00371-025-03908-7 (2025).","journal-title":"Vis. Comput."},{"key":"44111_CR42","doi-asserted-by":"publisher","unstructured":"Rathnakumari, L. & Rao, G. R. K. Enhancing heart disease prediction through a CNN-GAN hybrid deep learning model. SN Comput. Sci. 7, 47 https:\/\/doi.org\/10.1007\/s42979-025-04601-1. (2026).","DOI":"10.1007\/s42979-025-04601-1"},{"key":"44111_CR43","doi-asserted-by":"publisher","first-page":"3371","DOI":"10.1007\/s00371-024-03608-8","volume":"41","author":"X Liu","year":"2025","unstructured":"Liu, X. et al. Toward the unification of generative and discriminative visual foundation model: a survey. Vis. Comput. 41, 3371\u20133412. https:\/\/doi.org\/10.1007\/s00371-024-03608-8 (2025).","journal-title":"Vis. Comput."},{"key":"44111_CR44","doi-asserted-by":"publisher","unstructured":"Brimos, P., Seregkos, P., Karamanou, A., Kalampokis, E. & Tarabanis, K. Deep learning missing value imputation on traffic data using self-attention and GAN-based methods. In: Proceedings of the 2024 Panhellenic Conference on Electronics & Telecommunications (PACET), 1\u20134 (2024). https:\/\/doi.org\/10.1109\/PACET60398.2024.10497055","DOI":"10.1109\/PACET60398.2024.10497055"},{"key":"44111_CR45","doi-asserted-by":"publisher","first-page":"4639","DOI":"10.1007\/s00371-024-03682-y","volume":"41","author":"Y Tian","year":"2025","unstructured":"Tian, Y. et al. DGL-GAN: discriminator-guided GAN compression. Vis. Comput. 41, 4639\u20134660. https:\/\/doi.org\/10.1007\/s00371-024-03682-y (2025).","journal-title":"Vis. Comput."},{"key":"44111_CR46","doi-asserted-by":"crossref","unstructured":"Liang, H. & Wang, R. Research on multi-feature fusion shadow puppet motifs generation based on CSPMotifsGAN and cultural heritage preservation. Comput. Animat. Virtual Worlds 363, e70047 (2025).","DOI":"10.22541\/au.174643056.61989411\/v1"},{"key":"44111_CR47","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s00138-024-01543-1","volume":"35","author":"S Sarker","year":"2024","unstructured":"Sarker, S. et al. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation. Mach. Vis. Appl. 35, 67. https:\/\/doi.org\/10.1007\/s00138-024-01543-1 (2024).","journal-title":"Mach. Vis. Appl."},{"key":"44111_CR48","doi-asserted-by":"publisher","first-page":"106479","DOI":"10.1016\/j.compstruc.2021.106479","volume":"247","author":"A Shishegaran","year":"2021","unstructured":"Shishegaran, A., Varaee, H., Rabczuk, T. & Shishegaran, G. High correlated variables creator machine: Prediction of the compressive strength of concrete. Comput. Struct. 247, 106479 (2021). https:\/\/arxiv.org\/abs\/2009.06421","journal-title":"Comput. Struct."},{"key":"44111_CR49","doi-asserted-by":"publisher","unstructured":"Shishegaran, A. Computational methods for shape prediction for steel plate with stiffener subjected to explosive loads (Institut f\u00fcr Strukturmechanik, 2025). https:\/\/doi.org\/10.25643\/dbt.66715","DOI":"10.25643\/dbt.66715"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44111-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44111-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44111-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T22:48:30Z","timestamp":1777848510000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-44111-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["44111"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-44111-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,18]]},"assertion":[{"value":"3 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14076"}}