{"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/using-the-polynomial-particle-in-cell-method","title":"Using The Polynomial Particle-In-Cell Method For Liquid-Fabric Interaction","arxiv_id":"2308.01060","date":"2023-08-02","proceeding":null,"authors":["Robert Dennison","Steve Maddock"],"abstract":"Liquid-fabric interaction simulations using particle-in-cell (PIC) based models have been used to simulate a wide variety of phenomena and yield impressive visual results. However, these models suffer from numerical damping due to the data interpolation between the particles and grid. Our paper addresses this by using the polynomial PIC (PolyPIC) model instead of the affine PIC (APIC) model that is used in current state-of-the-art wet cloth models. The affine transfers of the APIC model are replaced by the higher order polynomials of PolyPIC, thus reducing numerical dissipation and improving resolution of vorticial details. This improved energy preservation enables more dynamic simulations to be generated although this is at an increased computational cost.","url_abs":"https://arxiv.org/abs/2308.01060v2","url_pdf":"https://arxiv.org/pdf/2308.01060v2.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":"using-the-polynomial-particle-in-cell-method","repo_url":"https://github.com/robden820/libwetcloth","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}