{"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/machine-learning-for-semi-linear-pdes","title":"Machine Learning for semi linear PDEs","arxiv_id":"1809.07609","date":"2018-09-20","proceeding":null,"authors":["Quentin Chan-Wai-Nam","Joseph Mikael","Xavier Warin"],"abstract":"Recent machine learning algorithms dedicated to solving semi-linear PDEs are\nimproved by using different neural network architectures and different\nparameterizations. These algorithms are compared to a new one that solves a\nfixed point problem by using deep learning techniques. This new algorithm\nappears to be competitive in terms of accuracy with the best existing\nalgorithms.","url_abs":"http://arxiv.org/abs/1809.07609v2","url_pdf":"http://arxiv.org/pdf/1809.07609v2.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":"machine-learning-for-semi-linear-pdes","repo_url":"https://gitlab.com/14chanwa/ml_for_semilinear_pdes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.07609","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}