VortexNet: Neural Computing through Fluid Dynamics
This repository contains toy implementations of the concepts introduced in the research paper VortexNet: Neural Computing through Fluid Dynamics. These examples demonstrate how PDE-based vortex layers and fluid-inspired mechanisms can be integrated into neural architectures, such as autoencoders for different datasets.
Note: These are toy prototypes for educational purposes and are not intended as fully optimized or physically precise fluid solvers.
Contents
vortexnet_mnist.py :
A demonstration script for building and training a VortexNet Autoencoder on the MNIST dataset.
: A demonstration script for building and training a VortexNet Autoencoder on the MNIST dataset. vortexnext_image.py :
An advanced script for building and training a VortexNet Autoencoder on custom image datasets with enhanced features like data augmentation and latent space interpolation.
Getting Started
1. Clone the Repository
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