This repository contains the source code for the research paper "Physics-Informed Shadowgraph Density Field Reconstruction".

Overview

This code implements a physics-informed framework for reconstructing density fields from shadowgraph images. The approach combines shadowgraph imaging techniques with physics-informed neural networks (PINNs) to capture refractive index variations in complex flow fields accurately.

The video below demonstrates the on-time prediction. Due to the lack of high-performance GPU support on this laptop, the prediction process is relatively slow. (P.S.: Our model was definitely not trained on this laptop! 😊)

Key Features

• Shadowgraph Image Processing: Pre-processing and analysis of shadowgraph images for density field visualization.
• PINN Implementation: Physics-informed neural network setup tailored for accurate density field reconstruction.
• Density Field Reconstruction: Algorithms for computing density distributions based on refractive index variations within the experimental field.

Dataset

The data folder only contains a few example images. Its purpose is to illustrate what the shadowgraph image looks like. Training solely on this data will result in significant overfitting. A complete example dataset can be obtained by contacting the authors.

Requirements

All necessary dependencies are listed in requirements.txt.

License

This code is for academic use ONLY. Please cite the original paper if you use this code in your research.

Authors

• Primary Author: Xutun Wang, Yuchen Zhang
• Contact Information: You can reach us at [xt-wang24@mails.tsinghua.edu.cn] or through our academic institution profiles.
• Special thanks to Dr. Yuchen Zhang @paradoxknight1 for his significant contributions to this research.