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When engineers or designers want to test the aerodynamic properties of the newly designed shape of a car, airplane, or other object, they would normally model the flow of air around the object by having a computer solve a complex set of equations—a procedure that usually takes hours, or even an entire day. Nobuyuki Umetani from Autodesk research (now at the University of Tokyo) and Bernd Bickel from the Institute of Science and Technology Austria (IST Austria) have now significantly sped up this process, making streamlines and parameters available in real time. Their method, which is the first to use machine learning to model flow around continuously editable 3-D objects, will be presented at this year's prestigious SIGGRAPH conference in Vancouver, where IST Austria researchers are involved in a total of five presentations.
Machine learning can make extremely time-consuming methods a lot faster. Before, the computation of the aerodynamic properties of cars usually took a day. "With our machine learning tool, we are able to predict the flow in fractions of a second," says Nobuyuki Umetani. The idea to use machine learning came up in a discussion between the two long-time collaborators. "We both share the vision of making simulations faster," explains IST Austria Professor Bernd Bickel. "We want people to be able to design objects interactively, and therefore we work together to develop data-driven methods," he adds.
Machine - Problem - Fields - Objects - Requirements
So far, it has been extremely challenging to apply machine learning to the problem of modeling flow fields around objects because of the restrictive requirements of the method. For machine learning, both the input and the output data need to be structured consistently. This structuring of...
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