Sample codes of CNN-SINDy based reduced-order modeling for fluid flows.
The present model can derive a governing equation of low-dimensionalizaed manifolds of fluid flows extracted via convolutional neural network-based autoencoder.
Kai Fukami (UCLA), Takaaki Murata (Keio), Kai Zhang (Rutgers Univ.), and Koji Fukagata (Keio), "Sparse identification of nonlinear dynamics with low-dimensionalized flow representations," Journal of Fluid Mechanics, 926, A10, preprint: arXiv:2010.12177, 2021
Author: Kai Fukami (UCLA)
This repository contains a sample notebook of SINDy for AE-based latent variables with a periodic cylinder wake. Authors provide no guarantees for this code. Use as-is and for academic research use only; no commercial use allowed without permission. The code is written for educational clarity and not for speed. Since this is a sample notebook, we also do not provide a data set and CNN-AE. For the data set and CNN-AE, please refer to our previous papars with their sample codes as follows:
- T. Murata, K. Fukami, and K. Fukagata, "Nonlinear mode decomposition with convolutional neural networks for fluid dynamics," J. Fluid Mech. 882, A13 (2020).
- K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata, "CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers," Fluid Dyn. Res. 52, 065501 (2020).
- Python 3.x
- keras
- tensorflow
- sklearn
- numpy
- pandas