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Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; Idomura, Yasuhiro; Onodera, Naoyuki; et al.
no journal, ,
We have established an in-situ data analysis method for large scale fluid simulation data and developed deep learning based surrogate models to predict fluid simulation results. Firstly, we have developed an in-situ data processing approach, which loosely couples the MPI application and python scripts. It has been shown that this approach is simple and efficient which offers the speedup of 2.7 compared to post hoc data processing. Secondly, we have developed a deep learning model for predicting multiresolution steady flow fields. The deep learning model can give reasonably accurate predictions of simulation results with orders of magnitude faster compared to simulations.
Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro; Asahi, Yuichi; Kawamura, Takuma; Ina, Takuya; Shimomura, Kazuya; Inagaki, Atsushi*; Suzuki, Shinichi*; Hirano, Kohin*; et al.
no journal, ,
Wind prediction based on digital twin is a promising technology that can contribute to the construction of new social infrastructures, including applications to smart city design and operation. In this poster presentation, we will introduce wind simulations based on data assimilation with observations and mesoscale meteorological data for the realization of a digital twin of wind conditions in urban areas.
Onodera, Naoyuki; Aoki, Takayuki*; Idomura, Yasuhiro; Yamashita, Susumu; Kawamura, Takuma; Asahi, Yuichi; Ina, Takuya; Hasegawa, Yuta; Sugihara, Kenta; Shimokawabe, Takashi*; et al.
no journal, ,
no abstracts in English
Sugihara, Kenta; Aoki, Takayuki*; Onodera, Naoyuki; Idomura, Yasuhiro; Kawamura, Takuma; Shimokawabe, Takashi*; Ina, Takuya; Yamashita, Susumu
no journal, ,
no abstracts in English
Asahi, Yuichi; Maeyama, Shinya*; Bigot, J.*; Garbet, X.*; Grandgirard, V.*; Obrejan, K.*; Padioleau, T.*; Fujii, Keisuke*; Shimokawabe, Takashi*; Watanabe, Tomohiko*; et al.
no journal, ,
We will demonstrate the performance portable implementation of a kinetic plasma code over CPUs, Nvidia and AMD GPUs. We will also discuss the performance portability of the code with C++ parallel algorithm. Deep learning based surrogate models for fluid simulations will also be demonstrated.