Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network

Cheng M.; Fang F.; Pain C. C.; Navon I. M.

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Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network

机译：使用深卷积生成对抗网络的非线性时空流体流动的数据驱动建模

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《Computer Methods in Applied Mechanics and Engineering》 |2020年第15期|113000.1-113000.18|共18页
作者
Cheng M.; Fang F.; Pain C. C.; Navon I. M.;
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作者单位

Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London SW7 2BP England;

Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London SW7 2BP England;

Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London SW7 2BP England;

Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
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7. Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network [O] . M. Cheng, F. Fang, C.C. Pain, 2020

机译：使用深卷积生成对抗网络的非线性时空流体流动的数据驱动建模

Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network

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