Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

Kai Fukami, Kazuto Hasegawa, Taichi Nakamura, Masaki Morimoto, Koji Fukagata

研究成果: Article査読

9 被引用数 (Scopus)

抄録

We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical fluid flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, and (4) a cross-sectional field of turbulent channel flow, in terms of a number of latent modes, the choice of nonlinear activation functions, and the number of weights contained in the AE model. We find that the AE models are sensitive to the choice of the aforementioned parameters depending on the target flows. Finally, we foresee the extensional applications and perspectives of machine learning based order reduction for numerical and experimental studies in the fluid dynamics community.

本文言語English
論文番号467
ジャーナルSN Computer Science
2
6
DOI
出版ステータスPublished - 2021 11月

ASJC Scopus subject areas

  • 計算理論と計算数学
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • コンピュータ サイエンス(全般)
  • 人工知能
  • コンピュータ グラフィックスおよびコンピュータ支援設計

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