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Multi-resolution steady flow prediction with convolutional neural networks

Asahi, Yuichi  ; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki  ; Hasegawa, Yuta  ; Idomura, Yasuhiro  

We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model Pix2PixHD, our model can predict the high resolution flow field from the signed distance function. By patching the high resolution data, the memory requirements in our model is suppressed compared to Pix2PixHD.

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