{"_id":"4","oa":1,"scopus_import":"1","publication":"ACM Trans. Graph.","language":[{"iso":"eng"}],"publication_status":"published","date_created":"2018-12-11T11:44:06Z","abstract":[{"text":"We present a data-driven technique to instantly predict how fluid flows around various three-dimensional objects. Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. To accelerate the process, we propose a machine learning framework which predicts aerodynamic forces and velocity and pressure fields given a threedimensional shape input. Handling detailed free-form three-dimensional shapes in a data-driven framework is challenging because machine learning approaches usually require a consistent parametrization of input and output. We present a novel PolyCube maps-based parametrization that can be computed for three-dimensional shapes at interactive rates. This allows us to efficiently learn the nonlinear response of the flow using a Gaussian process regression. We demonstrate the effectiveness of our approach for the interactive design and optimization of a car body.","lang":"eng"}],"volume":37,"file_date_updated":"2020-07-14T12:46:22Z","article_processing_charge":"No","isi":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","department":[{"_id":"BeBi"}],"publisher":"ACM","day":"04","file":[{"date_created":"2018-12-12T10:16:28Z","checksum":"7a2243668f215821bc6aecad0320079a","access_level":"open_access","file_name":"IST-2018-1049-v1+1_2018_sigg_Learning3DAerodynamics.pdf","file_id":"5216","creator":"system","date_updated":"2020-07-14T12:46:22Z","content_type":"application/pdf","relation":"main_file","file_size":22803163}],"doi":"10.1145/3197517.3201325","has_accepted_license":"1","type":"journal_article","article_number":"89","ddc":["003","004"],"issue":"4","author":[{"first_name":"Nobuyuki","last_name":"Umetani","full_name":"Umetani, Nobuyuki"},{"first_name":"Bernd","orcid":"0000-0001-6511-9385","last_name":"Bickel","full_name":"Bickel, Bernd","id":"49876194-F248-11E8-B48F-1D18A9856A87"}],"status":"public","external_id":{"isi":["000448185000050"]},"date_updated":"2023-09-13T08:46:15Z","publist_id":"8053","oa_version":"Submitted Version","year":"2018","pubrep_id":"1049","ec_funded":1,"month":"08","project":[{"grant_number":"715767","_id":"24F9549A-B435-11E9-9278-68D0E5697425","name":"MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling","call_identifier":"H2020"}],"related_material":{"link":[{"url":"https://ist.ac.at/en/news/new-interactive-machine-learning-tool-makes-car-designs-more-aerodynamic/","description":"News on IST Homepage","relation":"press_release"}]},"quality_controlled":"1","title":"Learning three-dimensional flow for interactive aerodynamic design","date_published":"2018-08-04T00:00:00Z","citation":{"chicago":"Umetani, Nobuyuki, and Bernd Bickel. “Learning Three-Dimensional Flow for Interactive Aerodynamic Design.” <i>ACM Trans. Graph.</i> ACM, 2018. <a href=\"https://doi.org/10.1145/3197517.3201325\">https://doi.org/10.1145/3197517.3201325</a>.","ista":"Umetani N, Bickel B. 2018. Learning three-dimensional flow for interactive aerodynamic design. ACM Trans. Graph. 37(4), 89.","apa":"Umetani, N., &#38; Bickel, B. (2018). Learning three-dimensional flow for interactive aerodynamic design. <i>ACM Trans. Graph.</i> ACM. <a href=\"https://doi.org/10.1145/3197517.3201325\">https://doi.org/10.1145/3197517.3201325</a>","short":"N. Umetani, B. Bickel, ACM Trans. Graph. 37 (2018).","mla":"Umetani, Nobuyuki, and Bernd Bickel. “Learning Three-Dimensional Flow for Interactive Aerodynamic Design.” <i>ACM Trans. Graph.</i>, vol. 37, no. 4, 89, ACM, 2018, doi:<a href=\"https://doi.org/10.1145/3197517.3201325\">10.1145/3197517.3201325</a>.","ieee":"N. Umetani and B. Bickel, “Learning three-dimensional flow for interactive aerodynamic design,” <i>ACM Trans. Graph.</i>, vol. 37, no. 4. ACM, 2018.","ama":"Umetani N, Bickel B. Learning three-dimensional flow for interactive aerodynamic design. <i>ACM Trans Graph</i>. 2018;37(4). doi:<a href=\"https://doi.org/10.1145/3197517.3201325\">10.1145/3197517.3201325</a>"},"intvolume":"        37"}