{"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/fast-gaussian-process-based-gradient-matching","title":"Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs","arxiv_id":"1804.04378","date":"2018-04-12","proceeding":null,"authors":["Philippe Wenk","Alkis Gotovos","Stefan Bauer","Nico Gorbach","Andreas Krause","Joachim M. Buhmann"],"abstract":"Parameter identification and comparison of dynamical systems is a challenging\ntask in many fields. Bayesian approaches based on Gaussian process regression\nover time-series data have been successfully applied to infer the parameters of\na dynamical system without explicitly solving it. While the benefits in\ncomputational cost are well established, a rigorous mathematical framework has\nbeen missing. We offer a novel interpretation which leads to a better\nunderstanding and improvements in state-of-the-art performance in terms of\naccuracy for nonlinear dynamical systems.","url_abs":"http://arxiv.org/abs/1804.04378v2","url_pdf":"http://arxiv.org/pdf/1804.04378v2.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":"fast-gaussian-process-based-gradient-matching","repo_url":"https://github.com/wenkph/FGPGM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"fast-gaussian-process-based-gradient-matching","repo_url":"https://github.com/jessiedbq/Auto_Sci-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"fast-gaussian-process-based-gradient-matching","repo_url":"https://github.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.04378","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}