{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T11:18:57Z","timestamp":1774005537904,"version":"3.50.1"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/T517987\/1"],"award-info":[{"award-number":["EP\/T517987\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer-Aided Design"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.cad.2026.104040","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T16:29:33Z","timestamp":1770049773000},"page":"104040","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Anisotropic mesh spacing prediction using neural networks"],"prefix":"10.1016","volume":"193","author":[{"given":"Callum","family":"Lock","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oubay","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0061-6214","authenticated-orcid":false,"given":"Ruben","family":"Sevilla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Jones","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.cad.2026.104040_b1","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1115\/1.1370162","article-title":"Reducing bottlenecks in the CAD-to-mesh-to-solution cycle time to allow CFD to participate in design","volume":"123","author":"Dawes","year":"2001","journal-title":"J Turbomach"},{"key":"10.1016\/j.cad.2026.104040_b2","unstructured":"Slotnick JP, Khodadoust A, Alonso J, Darmofal D, Gropp W, Lurie E, Mavriplis DJ. CFD vision 2030 study: a path to revolutionary computational aerosciences. Technical Report, 2014."},{"key":"10.1016\/j.cad.2026.104040_b3","doi-asserted-by":"crossref","unstructured":"Karman SL, Wyman N, Steinbrenner JP. Mesh generation challenges: A commercial software perspective. In: 23rd AIAA computational fluid dynamics conference. 2017, p. 3790.","DOI":"10.2514\/6.2017-3790"},{"key":"10.1016\/j.cad.2026.104040_b4","unstructured":"Michal T. Development of an anisotropic solution adaptive meshing tool for production aerospace applications. In: Sixth workshop on grid generation for numerical computations. 2019."},{"issue":"6","key":"10.1016\/j.cad.2026.104040_b5","doi-asserted-by":"crossref","first-page":"3791","DOI":"10.1007\/s00366-023-01812-z","article-title":"Meshing using neural networks for improving the efficiency of computer modelling","volume":"39","author":"Lock","year":"2023","journal-title":"Eng Comput"},{"key":"10.1016\/j.cad.2026.104040_b6","series-title":"Encyclopedia of computational mechanics second edition","article-title":"Mesh generation and mesh adaptivity: Theory and techniques","volume":"Part 1 Fundamentals","author":"George","year":"2017"},{"issue":"8","key":"10.1016\/j.cad.2026.104040_b7","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.1016\/j.jcp.2009.12.021","article-title":"Fully anisotropic goal-oriented mesh adaptation for 3D steady Euler equations","volume":"229","author":"Loseille","year":"2010","journal-title":"J Comput Phys"},{"issue":"5","key":"10.1016\/j.cad.2026.104040_b8","doi-asserted-by":"crossref","first-page":"4201","DOI":"10.1109\/20.105028","article-title":"A self-organizing neural network approach for automatic mesh generation","volume":"27","author":"Chang-Hoi","year":"1991","journal-title":"IEEE Trans Magn"},{"issue":"3","key":"10.1016\/j.cad.2026.104040_b9","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/TMAG.2003.810325","article-title":"A neural network generator for tetrahedral meshes","volume":"39","author":"Alfonzetti","year":"2003","journal-title":"IEEE Trans Magn"},{"key":"10.1016\/j.cad.2026.104040_b10","article-title":"MeshingNet3D: Efficient generation of adapted tetrahedral meshes for computational mechanics","volume":"157","author":"Zhang","year":"2021","journal-title":"Adv Eng Softw"},{"key":"10.1016\/j.cad.2026.104040_b11","series-title":"Machine learning-based optimal mesh generation in computational fluid dynamics","author":"Huang","year":"2021"},{"key":"10.1016\/j.cad.2026.104040_b12","doi-asserted-by":"crossref","unstructured":"Yang J, Dzanic T, Petersen B, Kudo J, Mittal K, Tomov V, Camier J-S, Zhao T, Zha H, Kolev T, et al. Reinforcement learning for adaptive mesh refinement. In: International conference on learning representations. 2022.","DOI":"10.65109\/WSQU5955"},{"key":"10.1016\/j.cad.2026.104040_b13","series-title":"E2N: error estimation networks for goal-oriented mesh adaptation","author":"Wallwork","year":"2022"},{"key":"10.1016\/j.cad.2026.104040_b14","series-title":"Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers","author":"Tlales","year":"2022"},{"key":"10.1016\/j.cad.2026.104040_b15","series-title":"SIAM international meshing roundtable 2023","first-page":"115","article-title":"Predicting the near-optimal mesh spacing for a simulation using machine learning","author":"Lock","year":"2024"},{"key":"10.1016\/j.cad.2026.104040_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2020.109957","article-title":"Metric-based, goal-oriented mesh adaptation using machine learning","volume":"426","author":"Fidkowski","year":"2021","journal-title":"J Comput Phys"},{"issue":"2\u20133","key":"10.1016\/j.cad.2026.104040_b17","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/10618562.2024.2306941","article-title":"A machine learning approach to predict near-optimal meshes for turbulent compressible flow simulations","volume":"38","author":"Sanchez-Gamero","year":"2024","journal-title":"Int J Comput Fluid Dyn"},{"key":"10.1016\/j.cad.2026.104040_b18","series-title":"Mesh generation: application to finite elements","author":"Frey","year":"2007"},{"key":"10.1016\/j.cad.2026.104040_b19","series-title":"Neural network design","author":"Hagan","year":"1997"},{"key":"10.1016\/j.cad.2026.104040_b20","series-title":"ADAM: A method for stochastic optimization","author":"Kingma","year":"2014"},{"issue":"2","key":"10.1016\/j.cad.2026.104040_b21","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/0021-9991(92)90401-J","article-title":"Adaptive remeshing for three-dimensional compressible flow computations","volume":"103","author":"Peraire","year":"1992","journal-title":"J Comput Phys"},{"issue":"7","key":"10.1016\/j.cad.2026.104040_b22","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1002\/nme.1620330702","article-title":"The superconvergent patch recovery and a posteriori error estimates. part 1: The recovery technique","volume":"33","author":"Zienkiewicz","year":"1992","journal-title":"Internat J Numer Methods Engrg"},{"issue":"7","key":"10.1016\/j.cad.2026.104040_b23","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1002\/nme.1620330703","article-title":"The superconvergent patch recovery and a posteriori error estimates. part 2: Error estimates and adaptivity","volume":"33","author":"Zienkiewicz","year":"1992","journal-title":"Internat J Numer Methods Engrg"},{"key":"10.1016\/j.cad.2026.104040_b24","series-title":"12th USeNIX symposium on operating systems design and implementation","first-page":"265","article-title":"TensorFlow: a system for Large-Scale machine learning","author":"Abadi","year":"2016"},{"issue":"1\u20132","key":"10.1016\/j.cad.2026.104040_b25","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s00466-002-0397-9","article-title":"A multigrid accelerated hybrid unstructured mesh method for 3D compressible turbulent flow","volume":"31","author":"S\u00f8rensen","year":"2003","journal-title":"Comput Mech"},{"issue":"12","key":"10.1016\/j.cad.2026.104040_b26","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/355588.365104","article-title":"Algorithm 247: Radical-inverse quasi-random point sequence","volume":"7","author":"Halton","year":"1964","journal-title":"Commun ACM"},{"issue":"1","key":"10.1016\/j.cad.2026.104040_b27","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.cam.2005.05.022","article-title":"Good permutations for deterministic scrambled Halton sequences in terms of L2-discrepancy","volume":"189","author":"Vandewoestyne","year":"2006","journal-title":"J Comput Appl Math"}],"container-title":["Computer-Aided Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010448526000102?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010448526000102?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:12:48Z","timestamp":1773997968000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010448526000102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":27,"alternative-id":["S0010448526000102"],"URL":"https:\/\/doi.org\/10.1016\/j.cad.2026.104040","relation":{},"ISSN":["0010-4485"],"issn-type":[{"value":"0010-4485","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Anisotropic mesh spacing prediction using neural networks","name":"articletitle","label":"Article Title"},{"value":"Computer-Aided Design","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cad.2026.104040","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104040"}}