AMR-Net: Convolutional Neural Networks for Multi-resolution Steady Flow Prediction

AMR-Net: Convolutional Neural Networks for Multi-resolution Steady Flow Prediction
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AMR-Net：用于多分辨率稳态流预测的卷积神经网络

DOI：
10.1109/cluster48925.2021.00102
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发表时间：
2021
期刊：
The 2nd Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, IEEE Cluster 2021
影响因子：
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通讯作者：
Idomura Yasuhiro
Idomura Yasuhiro
中科院分区：
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文献类型：
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作者：
Asahi Yuuichi;Hatayama Sora;Shimokawabe Takashi;Onodera Naoyuki;Hasegawa Yuta;Idomura Yasuhiro

文献摘要

相似文献

我们开发了一个卷积神经网络模型来预测多分辨率稳定流数据。基于图像到图像的翻译模型pix 2 pixHD，我们的模型可以预测高分辨率的流场从一组修补的有符号的距离函数。通过修补高分辨率数据，我们的模型使用的内存大约是pix 2 pixHD的三分之一。我们的模型的精度几乎是相同的U-Net模型使用未修补的高分辨率数据。
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.