{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-consistent-approach-to-unstructured-mesh","title":"A consistent approach to unstructured mesh generation for geophysical models","arxiv_id":"1703.08491","date":"2017-03-24","proceeding":null,"authors":["Adam S. Candy"],"abstract":"Geophysical model domains typically contain irregular, complex fractal-like boundaries and physical processes that act over a wide range of scales. Constructing geographically constrained boundary-conforming spatial discretizations of these domains with flexible use of anisotropically, fully unstructured meshes is a challenge. The problem contains a wide range of scales and a relatively large, heterogeneous constraint parameter space. Approaches are commonly ad hoc, model or application specific and insufficiently described. Development of new spatial domains is frequently time-consuming, hard to repeat, error prone and difficult to ensure consistent due to the significant human input required. As a consequence, it is difficult to reproduce simulations, ensure a provenance in model data handling and initialization, and a challenge to conduct model intercomparisons rigorously. Moreover, for flexible unstructured meshes, there is additionally a greater potential for inconsistencies in model initialization and forcing parameters. This paper introduces a consistent approach to unstructured mesh generation for geophysical models, that is automated, quick-to-draft and repeat, and provides a rigorous and robust approach that is consistent to the source data throughout. The approach is enabling further new research in complex multi-scale domains, difficult or not possible to achieve with existing methods. Examples being actively pursued in a range of geophysical modeling efforts are presented alongside the approach, together with the implementation library Shingle and a selection of its verification test cases.","url_abs":"http://arxiv.org/abs/1703.08491v1","url_pdf":"http://arxiv.org/pdf/1703.08491v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-consistent-approach-to-unstructured-mesh","repo_url":"https://github.com/adamcandy/Shingle","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-consistent-approach-to-unstructured-mesh","repo_url":"https://github.com/shingleproject/Shingle","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}