{"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-learning-of-vortex-induced-vibrations","title":"Deep Learning of Vortex Induced Vibrations","arxiv_id":"1808.08952","date":"2018-08-26","proceeding":null,"authors":["Maziar Raissi","Zhicheng Wang","Michael S. Triantafyllou","George Em. Karniadakis"],"abstract":"Vortex induced vibrations of bluff bodies occur when the vortex shedding\nfrequency is close to the natural frequency of the structure. Of interest is\nthe prediction of the lift and drag forces on the structure given some limited\nand scattered information on the velocity field. This is an inverse problem\nthat is not straightforward to solve using standard computational fluid\ndynamics (CFD) methods, especially since no information is provided for the\npressure. An even greater challenge is to infer the lift and drag forces given\nsome dye or smoke visualizations of the flow field. Here we employ deep neural\nnetworks that are extended to encode the incompressible Navier-Stokes equations\ncoupled with the structure's dynamic motion equation. In the first case, given\nscattered data in space-time on the velocity field and the structure's motion,\nwe use four coupled deep neural networks to infer very accurately the\nstructural parameters, the entire time-dependent pressure field (with no prior\ntraining data), and reconstruct the velocity vector field and the structure's\ndynamic motion. In the second case, given scattered data in space-time on a\nconcentration field only, we use five coupled deep neural networks to infer\nvery accurately the vector velocity field and all other quantities of interest\nas before. This new paradigm of inference in fluid mechanics for coupled\nmulti-physics problems enables velocity and pressure quantification from flow\nsnapshots in small subdomains and can be exploited for flow control\napplications and also for system identification.","url_abs":"http://arxiv.org/abs/1808.08952v1","url_pdf":"http://arxiv.org/pdf/1808.08952v1.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":"deep-learning-of-vortex-induced-vibrations","repo_url":"https://github.com/maziarraissi/DeepVIV","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.08952","atlas_url":"https://app.syntology.ai/?focus=1808.08952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.08952"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/maziarraissi/DeepVIV","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_violates":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"a7083d5006bc1338","entry":"figsize","repo":"maziarraissi/DeepVIV","repo_kind":"official","path":"plotting.py","file_url":"https://github.com/maziarraissi/DeepVIV/blob/HEAD/plotting.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a7083d5006bc1338"}},{"code_sha256_prefix":"5f2fe5254661d168","entry":"newfig","repo":"maziarraissi/DeepVIV","repo_kind":"official","path":"plotting.py","file_url":"https://github.com/maziarraissi/DeepVIV/blob/HEAD/plotting.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5f2fe5254661d168"}},{"code_sha256_prefix":"0b9238ff44895dc4","entry":"plot_solution","repo":"maziarraissi/DeepVIV","repo_kind":"official","path":"VIV_data_on_concentration.py","file_url":"https://github.com/maziarraissi/DeepVIV/blob/HEAD/VIV_data_on_concentration.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0b9238ff44895dc4"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}