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<titleInfo><title>Learning three-dimensional flow for interactive aerodynamic design</title></titleInfo>


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<name type="personal">
  <namePart type="given">Nobuyuki</namePart>
  <namePart type="family">Umetani</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
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  <namePart type="given">Bernd</namePart>
  <namePart type="family">Bickel</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">49876194-F248-11E8-B48F-1D18A9856A87</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0001-6511-9385</description></name>







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  <namePart>MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling</namePart>
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<abstract lang="eng">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.</abstract>

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<originInfo><publisher>ACM</publisher><dateIssued encoding="w3cdtf">2018</dateIssued>
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<relatedItem type="host"><titleInfo><title>ACM Trans. Graph.</title></titleInfo>
  <identifier type="ISI">000448185000050</identifier><identifier type="doi">10.1145/3197517.3201325</identifier>
<part><detail type="volume"><number>37</number></detail><detail type="issue"><number>4</number></detail>
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     <url>https://ist.ac.at/en/news/new-interactive-machine-learning-tool-makes-car-designs-more-aerodynamic/</url>
  
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<ama>Umetani N, Bickel B. Learning three-dimensional flow for interactive aerodynamic design. &lt;i&gt;ACM Trans Graph&lt;/i&gt;. 2018;37(4). doi:&lt;a href=&quot;https://doi.org/10.1145/3197517.3201325&quot;&gt;10.1145/3197517.3201325&lt;/a&gt;</ama>
<ista>Umetani N, Bickel B. 2018. Learning three-dimensional flow for interactive aerodynamic design. ACM Trans. Graph. 37(4), 89.</ista>
<chicago>Umetani, Nobuyuki, and Bernd Bickel. “Learning Three-Dimensional Flow for Interactive Aerodynamic Design.” &lt;i&gt;ACM Trans. Graph.&lt;/i&gt; ACM, 2018. &lt;a href=&quot;https://doi.org/10.1145/3197517.3201325&quot;&gt;https://doi.org/10.1145/3197517.3201325&lt;/a&gt;.</chicago>
<short>N. Umetani, B. Bickel, ACM Trans. Graph. 37 (2018).</short>
<apa>Umetani, N., &amp;#38; Bickel, B. (2018). Learning three-dimensional flow for interactive aerodynamic design. &lt;i&gt;ACM Trans. Graph.&lt;/i&gt; ACM. &lt;a href=&quot;https://doi.org/10.1145/3197517.3201325&quot;&gt;https://doi.org/10.1145/3197517.3201325&lt;/a&gt;</apa>
<ieee>N. Umetani and B. Bickel, “Learning three-dimensional flow for interactive aerodynamic design,” &lt;i&gt;ACM Trans. Graph.&lt;/i&gt;, vol. 37, no. 4. ACM, 2018.</ieee>
<mla>Umetani, Nobuyuki, and Bernd Bickel. “Learning Three-Dimensional Flow for Interactive Aerodynamic Design.” &lt;i&gt;ACM Trans. Graph.&lt;/i&gt;, vol. 37, no. 4, 89, ACM, 2018, doi:&lt;a href=&quot;https://doi.org/10.1145/3197517.3201325&quot;&gt;10.1145/3197517.3201325&lt;/a&gt;.</mla>
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