Machine Learning for Aerodynamic Uncertainty Quantification

Dishi?Liu; ?Daigo?Maruyama?; ?Stefan?G?rtz

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Machine Learning for Aerodynamic Uncertainty Quantification

机译：用于空气动力学不确定性量化的机器学习

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Within?the?framework?of?the?project?“Uncertainty?Management?for?Robust?Industrial?Design?in?Aeronautics”(UMRIDA),?funded?by?the?European?Union,?several?machine?learning-based?predictive?models?were?compared?interms?of?their?efficiency?in?estimating?statistics?of?aerodynamic?performance?of?aerofoils.?The?results?show?that?themodels?based?on?both?samples?and?gradients?achieve?better?accuracy?than?those?based?solely?on?samples?at?thesame?computational?costs.

机译：在？？框架？的？项目？“不确定性？管理？为鲁棒？工业？设计？在？在？欧洲？欧洲？联盟，？几个？学习 - 基于？预测性？模型？比较了吗？互联网？他们的？效率？估计？统计？呢？出现？结果？显示？基于？两者？样本？和？梯度？达到更好？准确性？比？那些？基于？单独？上？样品？在？Chesame？计算？成本。

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《ERCIM News》 |2019年第112期|共4页
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Dishi?Liu; ?Daigo?Maruyama?; ?Stefan?G?rtz;
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机译：几何不确定性跨肿大空气动力学的不确定性量化及敏感性分析

Machine Learning for Aerodynamic Uncertainty Quantification

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