{"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/deep-reinforcement-learning-achieves-flow","title":"Deep Reinforcement Learning achieves flow control of the 2D Karman Vortex Street","arxiv_id":"1808.10754","date":"2018-08-31","proceeding":null,"authors":["Jean Rabault","Ulysse Reglade","Nicolas Cerardi","Miroslav Kuchta","Atle Jensen"],"abstract":"The Karman Vortex Street has been investigated for over a century and offers a reference case for investigation of flow stability and control of high dimensionality, non-linear systems. Active flow control, while of considerable interest from a theoretical point of view and for industrial applications, has remained inaccessible due to the difficulty in finding successful control strategies. Here we show that Deep Reinforcement Learning can achieve a stable active control of the Karman vortex street behind a two-dimensional cylinder. Our results show that Deep Reinforcement Learning can be used to design active flow controls and is a promising tool to study high dimensionality, non-linear, time dependent dynamic systems present in a wide range of scientific problems.","url_abs":"http://arxiv.org/abs/1808.10754v1","url_pdf":"http://arxiv.org/pdf/1808.10754v1.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":"deep-reinforcement-learning-achieves-flow","repo_url":"https://github.com/aero-cfd/Cylinder2DFlowControlDRL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-reinforcement-learning-achieves-flow","repo_url":"https://github.com/jerabaul29/Cylinder2DFlowControlDRL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"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}