{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T12:51:28Z","timestamp":1766062288230,"version":"3.48.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005315","name":"Natural Science Foundation of Ningbo Municipality","doi-asserted-by":"publisher","award":["2023J113"],"award-info":[{"award-number":["2023J113"]}],"id":[{"id":"10.13039\/501100005315","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022955","name":"Fundamental Research Funds for the Provincial Universities of Zhejiang","doi-asserted-by":"publisher","award":["SJLZ2023001"],"award-info":[{"award-number":["SJLZ2023001"]}],"id":[{"id":"10.13039\/100022955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s00366-025-02202-3","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T10:16:18Z","timestamp":1758017778000},"page":"4345-4363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TetSimNet: a tetrahedral mesh simplification network model for preserving analysis accuracy"],"prefix":"10.1007","volume":"41","author":[{"given":"Songyuan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hongfei","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Junhe","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"2202_CR1","unstructured":"Staadt O, Gross M Progressive tetrahedralizations. In: Proceedings visualization\u201998 (Cat. No. 98CB36276)"},{"issue":"3","key":"2202_CR2","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/2945.795214","volume":"5","author":"IJ Trotts","year":"1999","unstructured":"Trotts IJ, Hamann B, Joy KI (1999) Simplification of tetrahedral meshes with error bounds. IEEE Trans Visual Comput Graphics 5(3):224\u2013237","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"2202_CR3","doi-asserted-by":"crossref","unstructured":"Cignoni P, Costanza D, Montani C, Rocchini C, Scopigno R (2000) Simplification of tetrahedral meshes with accurate error evaluation. In: Proceedings visualization 2000. VIS 2000 (Cat. No. 00CH37145), pp. 85\u201392. IEEE","DOI":"10.1109\/VISUAL.2000.885680"},{"issue":"5","key":"2202_CR4","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1109\/TVCG.2004.32","volume":"10","author":"V Natarajan","year":"2004","unstructured":"Natarajan V, Edelsbrunner H (2004) Simplification of three-dimensional density maps. IEEE Trans Visual Comput Graphics 10(5):587\u2013597","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"2202_CR5","unstructured":"Str\u00f6ter D, Stork A, Fellner DW (2023) Massively parallel adaptive collapsing of edges for unstructured tetrahedral meshes"},{"issue":"8","key":"2202_CR6","doi-asserted-by":"publisher","first-page":"4997","DOI":"10.1109\/TVCG.2023.3281781","volume":"30","author":"N Lei","year":"2023","unstructured":"Lei N, Li Z, Xu Z, Li Y, Gu X (2023) What\u2019s the situation with intelligent mesh generation: a survey and perspectives. IEEE Trans Visual Comput Graphics 30(8):4997\u20135017","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"2202_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2023.102109","volume":"72","author":"H Tong","year":"2023","unstructured":"Tong H, Qian K, Halilaj E, Zhang YJ (2023) SRL-assisted AFM: generating planar unstructured quadrilateral meshes with supervised and reinforcement learning-assisted advancing front method. J Comput Sci 72:102109","journal-title":"J Comput Sci"},{"key":"2202_CR8","unstructured":"Chao T, Renje C (2023) Triangular mesh simplification method based on neural network. China Sciencepaper 18(7)"},{"issue":"4","key":"2202_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322959","volume":"38","author":"R Hanocka","year":"2019","unstructured":"Hanocka R, Hertz A, Fish N, Giryes R, Fleishman S, Cohen-Or D (2019) Meshcnn: a network with an edge. ACM Trans Graph (ToG) 38(4):1\u201312","journal-title":"ACM Trans Graph (ToG)"},{"key":"2202_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109500","volume":"128","author":"Y Liang","year":"2022","unstructured":"Liang Y, He F, Zeng X, Yu B (2022) Feature-preserved convolutional neural network for 3d mesh recognition. Appl Soft Comput 128:109500","journal-title":"Appl Soft Comput"},{"key":"2202_CR11","doi-asserted-by":"crossref","unstructured":"Potamias RA, Ploumpis S, Zafeiriou S (2022) Neural mesh simplification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 18583\u201318592","DOI":"10.1109\/CVPR52688.2022.01803"},{"issue":"3","key":"2202_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3506694","volume":"41","author":"S-M Hu","year":"2022","unstructured":"Hu S-M, Liu Z-N, Guo M-H, Cai J-X, Huang J, Mu T-J, Martin RR (2022) Subdivision-based mesh convolution networks. ACM Trans Graph (TOG) 41(3):1\u201316","journal-title":"ACM Trans Graph (TOG)"},{"key":"2202_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2025.103874","volume":"202","author":"R Wang","year":"2025","unstructured":"Wang R, Liu S, Yu J, Zhan H (2025) HMSimNet: a hexahedral mesh simplification network model for preserving analysis accuracy. Adv Eng Softw 202:103874","journal-title":"Adv Eng Softw"},{"key":"2202_CR14","doi-asserted-by":"crossref","unstructured":"Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The World Wide Web Conference, pp 417\u2013426","DOI":"10.1145\/3308558.3313488"},{"key":"2202_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.phycom.2023.102076","volume":"59","author":"NR Sivakumar","year":"2023","unstructured":"Sivakumar NR, Nagarajan SM, Devarajan GG, Pullagura L, Mahapatra RP (2023) Enhancing network lifespan in wireless sensor networks using deep learning based graph neural network. Phys Commun 59:102076","journal-title":"Phys Commun"},{"key":"2202_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.690049","volume":"12","author":"X-M Zhang","year":"2021","unstructured":"Zhang X-M, Liang L, Liu L, Tang M-J (2021) Graph neural networks and their current applications in bioinformatics. Front Genet 12:690049","journal-title":"Front Genet"},{"issue":"1","key":"2202_CR17","first-page":"391","volume":"14","author":"X Chen","year":"2020","unstructured":"Chen X, Liu J, Pang Y, Chen J, Chi L, Gong C (2020) Developing a new mesh quality evaluation method based on convolutional neural network. Eng Appl Comput Fluid Mech 14(1):391\u2013400","journal-title":"Eng Appl Comput Fluid Mech"},{"issue":"5","key":"2202_CR18","doi-asserted-by":"publisher","first-page":"4631","DOI":"10.1007\/s00366-022-01677-8","volume":"38","author":"CL Chan","year":"2022","unstructured":"Chan CL, Scholz F, Takacs T (2022) Locally refined quad meshing for linear elasticity problems based on convolutional neural networks. Eng Comput 38(5):4631\u20134652","journal-title":"Eng Comput"},{"key":"2202_CR19","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.cag.2018.07.011","volume":"76","author":"P Wang","year":"2018","unstructured":"Wang P, Gan Y, Shui P, Yu F, Zhang Y, Chen S, Sun Z (2018) 3d shape segmentation via shape fully convolutional networks. Comput Graph 76:182\u2013192","journal-title":"Comput Graph"},{"key":"2202_CR20","doi-asserted-by":"crossref","unstructured":"Feng Y, Feng Y, You H, Zhao X, Gao Y (2019) Meshnet: mesh neural network for 3d shape representation. In: Proceedings of the AAAI conference on artificial intelligence, 33:8279\u20138286","DOI":"10.1609\/aaai.v33i01.33018279"},{"key":"2202_CR21","doi-asserted-by":"crossref","unstructured":"Verma N, Boyer E, Verbeek J (2018) Feastnet: Feature-steered graph convolutions for 3d shape analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2598\u20132606","DOI":"10.1109\/CVPR.2018.00275"},{"key":"2202_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104265","volume":"114","author":"W Tang","year":"2021","unstructured":"Tang W, Qiu G (2021) Dense graph convolutional neural networks on 3d meshes for 3d object segmentation and classification. Image Vis Comput 114:104265","journal-title":"Image Vis Comput"},{"key":"2202_CR23","unstructured":"Wang Z, Chen X, Yan J, Liu J (2023) Proposing an intelligent mesh smoothing method with graph neural networks. arXiv preprint arXiv:2311.12815"},{"issue":"1","key":"2202_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","volume":"6","author":"S Zhang","year":"2019","unstructured":"Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Comput Soc Netw 6(1):1\u201323","journal-title":"Comput Soc Netw"},{"key":"2202_CR25","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"2202_CR26","first-page":"7199","volume":"35","author":"W Song","year":"2022","unstructured":"Song W, Zhang M, Wallwork JG, Gao J, Tian Z, Sun F, Piggott M, Chen J, Shi Z, Chen X et al (2022) M2N: Mesh movement networks for PDE solvers. Adv Neural Inf Process Syst 35:7199\u20137210","journal-title":"Adv Neural Inf Process Syst"},{"key":"2202_CR27","unstructured":"Zhang H, Li H, Li N, Wang X (2023) Mqenet: A mesh quality evaluation neural network based on dynamic graph attention. arXiv preprint arXiv:2309.01067"},{"key":"2202_CR28","unstructured":"Diehl F (2019) Edge contraction pooling for graph neural networks. arXiv preprint arXiv:1905.10990"},{"issue":"3","key":"2202_CR29","first-page":"368","volume":"23","author":"L Haifeng","year":"2012","unstructured":"Haifeng L, Jichuan W, Jianbo L, Yubing L (2012) Finite element mesh generation and decision criteria of mesh quality. Chin Mech Eng 23(3):368\u2013377","journal-title":"Chin Mech Eng"},{"key":"2202_CR30","doi-asserted-by":"crossref","unstructured":"Chen X, Peng D, Gao S (2013) SVM-based topological optimization of tetrahedral meshes. In: Proceedings of the 21st International Meshing Roundtable, pp 211\u2013224. Springer","DOI":"10.1007\/978-3-642-33573-0_13"},{"issue":"12","key":"2202_CR31","doi-asserted-by":"publisher","first-page":"2560","DOI":"10.1109\/TVCG.2016.2632720","volume":"23","author":"K Hu","year":"2016","unstructured":"Hu K, Yan D-M, Bommes D, Alliez P, Benes B (2016) Error-bounded and feature preserving surface remeshing with minimal angle improvement. IEEE Trans Visual Comput Graphics 23(12):2560\u20132573","journal-title":"IEEE Trans Visual Comput Graphics"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-025-02202-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-025-02202-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-025-02202-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T12:48:48Z","timestamp":1766062128000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-025-02202-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2202"],"URL":"https:\/\/doi.org\/10.1007\/s00366-025-02202-3","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"type":"print","value":"0177-0667"},{"type":"electronic","value":"1435-5663"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"7 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}