Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes," Theor. Comput. Fluid Dyn. 34, 367-383 (2020). (Preprint: arXiv:2003.07548 [physics.flu-dyn]) These sample codes are not a stand-alone codes. The users should prepare own field data.
Flow fields computed by DNS (upper line) and predicted by ML-ROM (lower line). This figure shows velocity u, v and pressure p from left. Copyright © 2020 by the Springer.
Author: Kazuto Hasegawa (Keio University, Politecnico di Milano)
This repository consists
- Multi-Scale_CNN-AE.py (to create Multi-scale CNN-AE)
- LSTM_with_shape.py (to create LSTM model)
For citations, please use the reference below:
K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata,
"Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes,"
Theor. Comput. Fluid Dyn. 34, 367-383 (2020).
Kazuto Hasegawa provides 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.
* Python 3.x
* keras
* tensorflow
* sklearn
* numpy
* pandas
* tqdm
ML-ROM_Various_Shapes ── CNN_autoencoder/
├─ data ─── CNNAE ─── data_001.pickle ~ data080.pickle
│ │ └─ Test_data/data_001.pickle ~ data020.pickle
│ └─ LSTM ─── Dataset/
│ └─ Flags/
├─ .gitignore
├─ LSTM_with_shape.py
├─ Multi-Scale_CNN-AE.py
└─ README.md