/2D-3D-CNN

Primary LanguagePython

2D-3D CNN for three-dimensional reconstrcution from two-dimentional cross-sections of fluid flows

This repository contains sample source codes utilized in a part of "Supervised convolutional network for three-dimensional fluid data reconstrcution from sectional flow fields with adaptive super-resolution assistance," preprint: arXiv:2103.09020, 2021

A 2D-3D CNN is trained to estimate three-dimensional flow field from its two-dimensional cross-sections.

DNS ML

Flows around a square cylinder computed by direct numerical simulation (DNS) and estimated by ML from 5 cross-sections.

Original Adaptive sampling

Example of a adaptive-sampled field.

Information

Author: Mitsuaki Matsuo (Keio university)

This repository contains

  • 2D-3D-CNN.py
  • Adaptive-sampling.py

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.

Requirements

  • Python 3.x
  • Keras
  • Tensorflow
  • sklearn
  • numpy
  • skimage

Reference

M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, K. Fukagata, ``Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance," 2021 (preprint, arXiv:2103.09020 [physics.flu-dyn])