physics-informed-gaussians
[ICLR 2025] PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
Science Score: 41.0%
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Low similarity (11.9%) to scientific vocabulary
Repository
[ICLR 2025] PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
Basic Info
- Host: GitHub
- Owner: NamGyuKang
- Language: Python
- Default Branch: main
- Homepage: https://namgyukang.github.io/Physics-Informed-Gaussians/
- Size: 121 MB
Statistics
- Stars: 30
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
ICLR 2025
Namgyu Kang · Jaemin Oh · Youngjoon Hong · Eunbyung Park
Paper | Project Page
https://github.com/user-attachments/assets/ddfd6949-f459-40b8-bcb6-67558b980c09
https://github.com/user-attachments/assets/e6289032-aa43-4adc-a62f-8b015a0bf73f
https://github.com/user-attachments/assets/443ed3b0-f44c-49ae-8cac-f48e86704120
Quick Start
1. Installation
Clone Physics-Informed-Gaussians repo
git clone https://github.com/NamGyuKang/Physics-Informed-Gaussians.git
cd Physics-Informed-Gaussians
Create JAX environment (Flow-Mixing, Klein-Gordon, Nonliner-Diffusion Eq.)
Please follow the steps in the Jaxgpuversion_installation.txt file to install JAX GPU version.
Create Pytorch environment (Helmholtz Eq.)
The code is tested with Python (3.8, 3.9) and PyTorch (1.11, 11.2) with CUDA (>=11.3). You can create an anaconda environment with those requirements by running:
- if you use CUDA 11.3, Pytorch 1.11, Python 3.9,
conda env create -f CUDA_11_3_Pytorch_1_11_Py_3_9.yml - or with CUDA 11.6, Pytorch 1.12, Python 3.8,
conda env create -f CUDA_11_6_Pytorch_1_12_Py_3_8.yml - and then
conda activate pig
2. Run the code in each folder
CUDA_VISIBLE_DEVICES=0 bash flow_mixing3d_pig.shCUDA_VISIBLE_DEVICES=0 bash helmholtz2d_pig.shCUDA_VISIBLE_DEVICES=0 bash klein_gordon3d_pig.shCUDA_VISIBLE_DEVICES=0 bash diffusion3d_pig.sh
Citation
If you find this code useful in your research, please consider citing us!
bibtex
@article{kang2024pig,
title={PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations},
author={Kang, Namgyu and Oh, Jaemin and Hong, Youngjoon and Park, Eunbyung},
journal={arXiv preprint arXiv:2412.05994},
year={2024}
}
Contact
Contact Namgyu Kang if you have any further questions.
Acknowledgements
This project is built on top of several outstanding repositories: SPINN, PIXEL, JAXPI. We thank the original authors for opensourcing their excellent work.
Owner
- Login: NamGyuKang
- Kind: user
- Repositories: 3
- Profile: https://github.com/NamGyuKang
Citation (CITATION.bib)
@misc{kang2024pigphysicsinformedgaussiansadaptive,
title={PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations},
author={Namgyu Kang and Jaemin Oh and Youngjoon Hong and Eunbyung Park},
year={2024},
eprint={2412.05994},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2412.05994},
}
GitHub Events
Total
- Issues event: 1
- Watch event: 33
- Member event: 1
- Public event: 2
- Push event: 47
- Fork event: 2
- Create event: 3
Last Year
- Issues event: 1
- Watch event: 33
- Member event: 1
- Public event: 2
- Push event: 47
- Fork event: 2
- Create event: 3