by Yitong Deng, Hong-Xing Yu, Diyang Zhang, Jiajun Wu, and Bo Zhu.
Our paper and video results can be found at our project website.
Our code is tested on Windows 11
with CUDA 11.8
, Python 3.10.9
, PyTorch 2.0.1
, and Taichi 1.6.0
.
To set up the environment, first create a conda environment:
conda create -n "nfm_env" python=3.10.9 ipython
conda activate nfm_env
Then, install PyTorch with:
python -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118
Finally, install the requirements with:
pip install -r requirements.txt
For running simulation, simply execute:
python run.py
Hyperparameters can be tuned by changing the values in the file hyperparameters.py
. Checkpointing is available by setting the from_frame
variable to the desired frame, given that the checkpoint of that frame can be found in logs/[exp_name]/ckpts
.
The results will be stored in logs/[exp_name]/vtks
. We recommend using ParaView to load these .vti
files as a sequence and visualize them by selecting Volume
in the Representation
drop-down menu.
If you find our paper or code helpful, consider citing:
@article{deng2023neural,
title={Fluid Simulation on Neural Flow Maps},
author={Yitong Deng and Hong-Xing Yu and Diyang Zhang and Jiajun Wu and Bo Zhu},
journal={ACM Trans. Graph.},
volume={42},
number={6},
article={},
year={2023},
}