{"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/liquid-splash-modeling-with-neural-networks","title":"Liquid Splash Modeling with Neural Networks","arxiv_id":"1704.04456","date":"2017-04-14","proceeding":null,"authors":["Kiwon Um","Xiangyu Hu","Nils Thuerey"],"abstract":"This paper proposes a new data-driven approach to model detailed splashes for\nliquid simulations with neural networks. Our model learns to generate\nsmall-scale splash detail for the fluid-implicit-particle method using training\ndata acquired from physically parametrized, high resolution simulations. We use\nneural networks to model the regression of splash formation using a classifier\ntogether with a velocity modifier. For the velocity modification, we employ a\nheteroscedastic model. We evaluate our method for different spatial scales,\nsimulation setups, and solvers. Our simulation results demonstrate that our\nmodel significantly improves visual fidelity with a large amount of realistic\ndroplet formation and yields splash detail much more efficiently than finer\ndiscretizations.","url_abs":"http://arxiv.org/abs/1704.04456v2","url_pdf":"http://arxiv.org/pdf/1704.04456v2.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":"abstracts"},"code_links":[{"paper_slug":"liquid-splash-modeling-with-neural-networks","repo_url":"https://github.com/kiwonum/mlflip","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.04456","atlas_url":"https://app.syntology.ai/?focus=1704.04456","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}