AMR-Net: Convolutional Neural Networks for Multi-resolution Steady Flow Prediction
Yuuichi Asahi, Sora Hatayama, Takashi Shimokawabe, Naoyuki Onodera, Yuta Hasegawa, Yasuhiro Idomura · 2021
We develop a convolutional neural network model to predict multi-resolution steady flow data. Based on the image-to-image translation model pix2pixHD, our model can predict high resolution flow fields from the set of patched signed distance functions. By patching the high resolution data, our model uses roughly the one third of memory used by pix2pixHD. The accuracy of our model is almost the same as the U-Net model using the unpatched high resolution data.