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

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

Proceedings of 2021 IEEE International Conference on Cluster Computing (IEEE Cluster 2021) (Internet), p.686 - 691, 2021/10

https://doi.org/10.1109/Cluster48925.2021.00102

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.

Adaptive learning fuzzy control of a mobile robot

; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

JAERI-M 89-191, 25 Pages, 1989/11

JAERI-M-89-191.pdf:0.61MB

no abstracts in English