【摘要】 提出了一种将深度神经网络与流体模拟相结合的新方法。将具有更多湍流细节的高精度流体模拟结果看作图像中的"风格",利用训练好的深度神经网络的中间层提取特征信息。采用图像风格化技术,将高精度流体模拟结果的湍流信息迁移到低精度流体模拟结果中,使得低精度流体模拟结果同样具有丰富的湍流细节,实现了超分辨率的效果。实时完成低精度流体模拟和湍流迁移,实现了实时的高精度流体模拟。利用流体模拟中的速度信息保证流体模拟在时域上的连续性,使得整个模拟的结果更为真实。采用可以适用于任意风格输入的自适应的实例归一化(adaptive instance normalization,AdaIN)风格化技术,实现了流体模拟的艺术风格控制。更多还原

【Abstract】 We propose a novel method by combining deep neural network with fluid simulation.High-resolution results with more turbulence details are considered as‘style’and pre-trained networks such as VGG are used to extract features.Our method is capable of transferring turbulence details from high-resolution results to low-resolution ones,achieving real-time performance.The consistency of the whole simulation is guaranteed by the velocity field.Our method provides more artistic control by using the adaptive instance normalization(AdaIN)framework,which supports arbitrary style.更多还原