论文标题
深度学习对流流使用条件生成对抗网络
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
论文作者
论文摘要
我们开发了一个一般的深度学习框架，即Fluidgan，能够学习和预测时间依赖的对流流以及能量传输。 Fluidgan以高速和准确性彻底地数据驱动，并满足流体的物理，而没有任何先验的液体和能量运输物理学的知识。 Fluidgan还学习了速度，压力和温度场之间的耦合。我们的框架有助于理解基础物理模型复杂或未知的确定性多物理现象。
We developed a general deep learning framework, FluidGAN, capable of learning and predicting time-dependent convective flow coupled with energy transport. FluidGAN is thoroughly data-driven with high speed and accuracy and satisfies the physics of fluid without any prior knowledge of underlying fluid and energy transport physics. FluidGAN also learns the coupling between velocity, pressure, and temperature fields. Our framework helps understand deterministic multiphysics phenomena where the underlying physical model is complex or unknown.
