Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

By Takaaki Murata, Kai Fukami, Koji Fukagata. Journal of Fluid Mechanics, 2019

341 citations recorded by the source corpus.

We present a new nonlinear mode decomposition method to visualize decomposed flow fields, named the mode decomposing convolutional neural network autoencoder (MD-CNN-AE). The proposed method is applied to a flow around a circular cylinder at the Reynolds number $Re_{D}=100$ as a test case. The flow attributes are mapped into two modes in the latent space and then these two modes are visualized in the physical space. Because the MD-CNN-AEs with nonlinear activation functions show lower reconstruction errors than the proper orthogonal decomposition (POD), the nonlinearity contained in the activation function is considered the key to improving the capability of the model. It is found by applying POD to each field decomposed using the MD-CNN-AE with hyperbolic tangent activation such that a single nonlinear MD-CNN-AE mode contains multiple orthogonal bases, in contrast to the linear methods, i.e. POD and MD-CNN-AE with linear activation. We further assess the proposed MD-CNN-AE by applying it to a transient process of a circular cylinder wake in order to examine its capability for flows containing high-order spatial modes. The present results suggest a great potential for the nonlinear MD-CNN-AE to be used for feature extraction of flow fields in lower dimensions than POD, while retaining interpretable relationships with the conventional POD modes.

Subfields: Aerospace Engineering, Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4658b2e3ba420d3c8f99bc2d694fc49977a4e468

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In Aerospace Engineering

  1. Steven L. Brunton
  2. Ricardo Vinuesa
  3. J. Nathan Kutz

In Computational Mechanics

  1. Ricardo Vinuesa
  2. Steven L. Brunton
  3. J. Nathan Kutz
  4. Joshua L. Proctor
  5. Igor Mezi'c
  6. Bingni W. Brunton
  7. Eurika Kaiser
  8. Joseph Sang-Il Kwon

In Statistical and Nonlinear Physics

  1. Ricardo Vinuesa
  2. Steven L. Brunton
  3. J. Nathan Kutz
  4. Maizar Raissi
  5. Joshua L. Proctor
  6. Bingni W. Brunton
  7. Eurika Kaiser
  8. G. Em Karniadakis

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