AMR-Net: Convolutional neural networks for multi-resolution steady flow prediction

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 set of patched signed distance functions. By patching the high resolution data, the memory requirements in our model is suppressed compared to pix2pixHD.