学際大規模情報基盤共同利用・共同研究拠点

採択課題 【詳細】

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jh190070-MDJ 機械学習に基づく流体変数の未来予測と数学的背景
課題代表者 齊木吉隆(一橋大学) /
Yoshitaka Saiki(Hitotsubashi University)
概要 We construct a data-driven dynamical system model for a macroscopic variable of a high-dimensionally chaotic fluid flow by training its time-series data. We use a machine-learning approach, the reservoir computing for the construction of the model, and do not use the knowledge of a physical process of fluid dynamics in its procedure.
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JHPCN : Japan High Performance Computing and Networking plus Large-scale Data Analyzing and Information Systems
Update:2018.4.1