{"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/mad-ng-a-standalone-multiplatform-tool-for","title":"MAD-NG, a standalone multiplatform tool for linear and non-linear optics design and optimisation","arxiv_id":"2412.16006","date":"2024-12-20","proceeding":null,"authors":["Laurent Deniau"],"abstract":"The paper will provide an overview of the capabilities of the Methodical Accelerator Design Next Generation (MAD-NG) tool. MAD-NG is a standalone, all-in-one, multi-platform tool well-suited for linear and nonlinear optics design and optimization, and has already been used in large-scale studies such as HiLumi-LHC or FCC-ee. It embeds LuaJIT, an extremely fast tracing just-in-time compiler for the Lua programming language, delivering exceptional versatility and performance for the forefront of computational physics. The core of MAD-NG relies on the fast Generalized Truncated Power Series Algebra (GTPSA) library, which has been specially developed to handle many parameters and high-order differential algebra, including Lie map operators. This ecosystem offers powerful features for the analysis and optimization of linear and nonlinear optics, thanks to the fast parametric nonlinear normal forms and the polyvalent matching command. A few examples and results will complete this overview of the MAD-NG application.","url_abs":"https://arxiv.org/abs/2412.16006v2","url_pdf":"https://arxiv.org/pdf/2412.16006v2.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":"mad-ng-a-standalone-multiplatform-tool-for","repo_url":"https://github.com/methodicalacceleratordesign/mad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mad-ng-a-standalone-multiplatform-tool-for","repo_url":"https://github.com/MethodicalAcceleratorDesign/MAD-NG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}