{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T06:37:20Z","timestamp":1791355040542,"version":"4.3.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB0300101"],"award-info":[{"award-number":["2021YFB0300101"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00366-023-01861-4","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T10:01:36Z","timestamp":1686909696000},"page":"1111-1126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Developing an advanced neural network and physics solver coupled framework for accelerating flow field simulations"],"prefix":"10.1007","volume":"40","author":[{"given":"Xinhai","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiejun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunbo","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuechao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunye","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3745-7541","authenticated-orcid":false,"given":"Jie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"1861_CR1","volume-title":"An introduction to computational fluid dynamics","author":"H Versteeg","year":"1995","unstructured":"Versteeg H, Malalasekera HK (1995) An introduction to computational fluid dynamics. Prentice Hall - Pearson Education Limited, Essex, England"},{"key":"1861_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-021-01351-5","author":"Y He","year":"2021","unstructured":"He Y, Sun J, Song P, Wang X (2021) Multi-objective efficient global optimization of expensive simulation-based problem in presence of simulation failures. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-021-01351-5","journal-title":"Eng Comput"},{"key":"1861_CR3","doi-asserted-by":"publisher","unstructured":"Benjanirat S, Sankar L, Xu G (2003) Evaluation of turbulence models for the prediction of wind turbine aerodynamics. In: 41st Aerospace Sciences Meeting and Exhibit, Nevada. https:\/\/doi.org\/10.2514\/6.2003-517","DOI":"10.2514\/6.2003-517"},{"key":"1861_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2021.100725","volume":"125","author":"J Kou","year":"2021","unstructured":"Kou J, Zhang W (2021) Data-driven modeling for unsteady aerodynamics and aeroelasticity. Prog Aerosp Sci 125:100725. https:\/\/doi.org\/10.1016\/j.paerosci.2021.100725","journal-title":"Prog Aerosp Sci"},{"key":"1861_CR5","doi-asserted-by":"publisher","unstructured":"Zhang Y, Sung W, Mavris D (2018) Application of convolutional neural network to predict airfoil lift coefficient. In: 2018 AIAA\/ASCE\/AHS\/ASC Structures, Structural Dynamics, and Materials Conference. https:\/\/doi.org\/10.2514\/6.2018-1903","DOI":"10.2514\/6.2018-1903"},{"issue":"1","key":"1861_CR6","doi-asserted-by":"publisher","first-page":"25","DOI":"10.2514\/1.J058291","volume":"58","author":"N Thuerey","year":"2020","unstructured":"Thuerey N, Wei\u00dfenow K, Prantl L, Hu X (2020) Deep learning methods for reynolds-averaged Navier-Stokes simulations of airfoil flows. AIAA J 58(1):25\u201336","journal-title":"AIAA J"},{"key":"1861_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109339","volume":"410","author":"K Lye","year":"2020","unstructured":"Lye K, Mishra S, Ray D (2020) Deep learning observables in computational fluid dynamics. J Comput Phys 410:109339","journal-title":"J Comput Phys"},{"key":"1861_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-022-01632-7","author":"X Chen","year":"2022","unstructured":"Chen X, Li T, Wan Q, He X, Gong C, Pang Y, Liu J (2022) Mgnet: a novel differential mesh generation method based on unsupervised neural networks. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-022-01632-7","journal-title":"Eng Comput"},{"key":"1861_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2021.103104","volume":"141","author":"X Chen","year":"2021","unstructured":"Chen X, Liu J, Gong C, Li S, Pang Y, Chen B (2021) MVE-Net: An automatic 3-d structured mesh validity evaluation framework using deep neural networks. Comput Aided Des 141:103104. https:\/\/doi.org\/10.1016\/j.cad.2021.103104","journal-title":"Comput Aided Des"},{"key":"1861_CR10","doi-asserted-by":"publisher","unstructured":"Chen X, Chen R, Wan Q, Xu R, Liu J (2021) An improved data-free surrogate model for solving partial differential equations using deep neural networks. Sci Rep 11. https:\/\/doi.org\/10.1038\/s41598-021-99037-x","DOI":"10.1038\/s41598-021-99037-x"},{"issue":"2","key":"1861_CR11","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s00466-019-01740-0","volume":"64","author":"S Bhatnagar","year":"2019","unstructured":"Bhatnagar S, Afshar Y, Pan S, Duraisamy K, Kaushik S (2019) Prediction of aerodynamic flow fields using convolutional neural networks. Comput Mech 64(2):525\u2013545. https:\/\/doi.org\/10.1007\/s00466-019-01740-0","journal-title":"Comput Mech"},{"key":"1861_CR12","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686\u2013707. https:\/\/doi.org\/10.1016\/j.jcp.2018.10.045","journal-title":"J Comput Phys"},{"issue":"6481","key":"1861_CR13","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1126\/science.aaw4741","volume":"367","author":"M Raissi","year":"2020","unstructured":"Raissi M, Yazdani A, Karniadakis GE (2020) Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science 367(6481):1026\u20131030. https:\/\/doi.org\/10.1126\/science.aaw4741","journal-title":"Science"},{"key":"1861_CR14","doi-asserted-by":"publisher","unstructured":"Chen X, Chen R, Wan Q, Xu R, Liu J (2021) An improved data-free surrogate model for solving partial differential equations using deep neural networks. Sci Rep 11. https:\/\/doi.org\/10.1038\/s41598-021-99037-x","DOI":"10.1038\/s41598-021-99037-x"},{"key":"1861_CR15","doi-asserted-by":"crossref","unstructured":"Obiols-Sales O, Vishnu A, Malaya N, Chandramowliswharan A (2020) CFDNet: A deep learning-based accelerator for fluid simulations. In: Proceedings of the 34th ACM International Conference on Supercomputing, pp. 1\u201312","DOI":"10.1145\/3392717.3392772"},{"key":"1861_CR16","unstructured":"Belbute-Peres FdA, Economon T, Kolter Z (2020) Combining differentiable pde solvers and graph neural networks for fluid flow prediction. In: International Conference on Machine Learning, pp. 2402\u20132411. PMLR"},{"key":"1861_CR17","doi-asserted-by":"crossref","unstructured":"Tsunoda Y, Mori T, Akima H, Inano S, Tabaru T, Oyama A (2021) Accuracy improvement technique of DNN for accelerating CFD simulator","DOI":"10.2514\/6.2022-1437"},{"key":"1861_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-020-05461-x","volume":"33","author":"C Duru","year":"2021","unstructured":"Duru C, Alemdar H, Baran O (2021) CNNFOIL: convolutional encoder decoder modeling for pressure fields around airfoils. Neural Comput Appl 33:1\u201315. https:\/\/doi.org\/10.1007\/s00521-020-05461-x","journal-title":"Neural Comput Appl"},{"issue":"2","key":"1861_CR19","doi-asserted-by":"publisher","first-page":"173","DOI":"10.2514\/2.7499","volume":"36","author":"CG Speziale","year":"1998","unstructured":"Speziale CG (1998) Turbulence modeling for time-dependent RANS and VLES: A review. AIAA J 36(2):173\u2013184. https:\/\/doi.org\/10.2514\/2.7499","journal-title":"AIAA J"},{"key":"1861_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-022-01640-7","author":"SY Lee","year":"2022","unstructured":"Lee SY, Park C-S, Park K, Lee HJ, Lee S (2022) A physics-informed and data-driven deep learning approach for wave propagation and its scattering characteristics. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-022-01640-7","journal-title":"Eng Comput"},{"key":"1861_CR21","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1007\/978-3-030-05051-1_17","volume-title":"Algorithms and Architectures for Parallel Processing","author":"X Chen","year":"2018","unstructured":"Chen X, Liu J, Li S, Xie P, Chi L, Wang Q (2018) TAMM: A new topology-aware mapping method for parallel applications on the Tianhe-2A supercomputer. In: Vaidya J, Li J (eds) Algorithms and Architectures for Parallel Processing. Springer, Cham, pp 242\u2013256"},{"key":"1861_CR22","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1017\/jfm.2016.380","volume":"800","author":"R Semaan","year":"2016","unstructured":"Semaan R, Kumar P, Burnazzi M, Tissot G, Cordier L, Noack BR (2016) Reduced-order modelling of the flow around a high-lift configuration with unsteady coanda blowing. J Fluid Mech 800:72\u2013110. https:\/\/doi.org\/10.1017\/jfm.2016.380","journal-title":"J Fluid Mech"},{"key":"1861_CR23","doi-asserted-by":"publisher","unstructured":"Deng N, Noack BR, Morzy\u0144ski M, Pastur LR (2020) Low-order model for successive bifurcations of the fluidic pinball. J Fluid Mech 884:37. https:\/\/doi.org\/10.1017\/jfm.2019.959","DOI":"10.1017\/jfm.2019.959"},{"key":"1861_CR24","doi-asserted-by":"crossref","unstructured":"Mou C, Chen N, Iliescu T (2022) An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems","DOI":"10.1016\/j.jcp.2023.112450"},{"key":"1861_CR25","doi-asserted-by":"crossref","unstructured":"Charles RQ, Su H, Kaichun M, Guibas LJ (2017) PointNet: Deep learning on point sets for 3d classification and segmentation. In: 2017 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 77\u201385","DOI":"10.1109\/CVPR.2017.16"},{"key":"1861_CR26","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp 6105\u20136114. PMLR"},{"issue":"1","key":"1861_CR27","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"},{"key":"1861_CR28","unstructured":"Spini A (2020) A neural network investigation for an aerodynamic prediction. Master\u2019s thesis (January 2020)"},{"key":"1861_CR29","doi-asserted-by":"publisher","unstructured":"Scillitoe A, Seshadri P, Wong CY (2021) Instantaneous flowfield estimation with gaussian ridges. In: AIAA Scitech 2021 Forum. https:\/\/doi.org\/10.2514\/6.2021-1138","DOI":"10.2514\/6.2021-1138"},{"key":"1861_CR30","doi-asserted-by":"publisher","unstructured":"Donglin C, Gao X, Xu C, Chen S, Fang J, Wang Z (2020) FlowGAN: A conditional generative adversarial network for flow prediction in various conditions. In: 32nd International Conference on Tools with Artificial Intelligence.https:\/\/doi.org\/10.1109\/ICTAI50040.2020.00057","DOI":"10.1109\/ICTAI50040.2020.00057"},{"key":"1861_CR31","unstructured":"Pfaff T, Fortunato M, Sanchez-Gonzalez A, Battaglia PW (2021) Learning Mesh-Based Simulation with Graph Networks"},{"key":"1861_CR32","doi-asserted-by":"publisher","DOI":"10.1063\/5.0033376","volume":"33","author":"A Kashefi","year":"2021","unstructured":"Kashefi A, Rempe D, Guibas L (2021) A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries. Phys Fluids 33:027104. https:\/\/doi.org\/10.1063\/5.0033376","journal-title":"Phys Fluids"},{"key":"1861_CR33","unstructured":"Charles, R.Q., Yi, L., Su, H., Guibas, L.: PointNet++: Deep hierarchical feature learning on point sets in a metric space. Adv NeuralInform Process Syst 5099\u20135108 (2017)"},{"key":"1861_CR34","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201915, pp. 2017\u20132025. MIT Press, Cambridge, MA, USA (2015)"},{"key":"1861_CR35","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.11897\/SP.J.1016.2019.02368","volume":"42","author":"Z Zhao","year":"2019","unstructured":"Zhao Z, He L, Guo Y, He X, Zhang L (2019) PHengLEI: A large scale parallel cfd framework for arbitrary grids. Chin J Comput 42:2368\u20132383. https:\/\/doi.org\/10.11897\/SP.J.1016.2019.02368","journal-title":"Chin J Comput"},{"key":"1861_CR36","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp 265\u2013283. USENIX Association, Savannah, GA"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-023-01861-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-023-01861-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-023-01861-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,21]],"date-time":"2024-04-21T02:47:51Z","timestamp":1713667671000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-023-01861-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,16]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1861"],"URL":"https:\/\/doi.org\/10.1007\/s00366-023-01861-4","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"value":"0177-0667","type":"print"},{"value":"1435-5663","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,16]]},"assertion":[{"value":"29 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2023","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}