Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model (AAAI2024)

NOTE: We use the concepts of VQ-VAE and memory to improve Earthfarseer and add more experiments. Currently, it is submitted to TPAMI 2024 as Earthfarseer-V2.

Abstract

Efficiently modeling spatio-temporal physical processes presents a challenge for deep learning. Recent models often lack simplicity and practicality. To address this, we propose EarthFarseer, a concise framework combining parallel local convolutions and global Fourier-based transformer architectures. This approach dynamically captures local-global spatial interactions and dependencies. EarthFarseer also incorporates multi-scale fully convolutional and Fourier architectures for efficient temporal evolution. It demonstrates strong adaptability, fast convergence, and improved local fidelity in long-term predictions across various datasets, achieving state-of-the-art performance.

Getting Started

Install Python 3.8. For convenience, execute the following command.

pip install -r requirements.txt

Prepare Data. You can obtain experimental datasets from the following links.

Dataset	Task	Geometry	Link
Navier-Stokes equation	Predict future fluid vorticity	Regular Grid	[Google Cloud]
Shallow-water equations	Predict future fluid height	Regular Grid	[Google Cloud]
Moving MNIST	Predict future image	Regular Grid	[Google Cloud]

Use the following instructions to quickly run the code.

python train_main.py --data_path Dataset/NavierStokes_V1e-5_N1200_T20.mat --num_epochs 100 --batch_size 5

Citation

If you are interested in our repository or our paper, please cite the following paper:

@inproceedings{wu2024earthfarsser,
  title={Earthfarsser: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model},
  author={Wu, Hao and Liang, Yuxuan and Xiong, Wei and Zhou, Zhengyang and Huang, Wei and Wang, Shilong and Wang, Kun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={14},
  pages={15906--15914},
  year={2024}
}

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