Summary of Enhanced Spatiotemporal Prediction Using Physical-guided and Frequency-enhanced Recurrent Neural Networks, by Xuanle Zhao et al.
Enhanced Spatiotemporal Prediction Using Physical-guided And Frequency-enhanced Recurrent Neural Networks
by Xuanle Zhao, Yue Sun, Tielin Zhang, Bo Xu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed physical-guided neural network utilizes frequency-enhanced Fourier modules and moment loss to strengthen its ability to estimate spatiotemporal dynamics. This approach incorporates prior physical knowledge into the deep learning framework to estimate governing partial differential equations (PDEs), which has shown promising results in spatiotemporal prediction tasks. The model is evaluated on both spatiotemporal and video prediction tasks, outperforming state-of-the-art methods and achieving best performance in several datasets with a smaller parameter count. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to predict the future based on past information. It uses a special type of neural network that takes into account physical laws like those that govern weather patterns or human movements. The model is tested on different tasks, such as predicting what will happen next in a video or forecasting the weather. The results show that this approach outperforms other methods and can achieve good results even with fewer calculations. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Spatiotemporal
