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[2405.14504] Adaptive Runge-Kutta Dynamics for Spatiotemporal Prediction
Summary
The paper presents an innovative approach using an adaptive Runge-Kutta method for spatiotemporal prediction, enhancing model accuracy in dynamic scenarios.
Why It Matters
This research addresses the limitations of existing neural network architectures in modeling physical dynamics, which is crucial for applications like weather forecasting and video analysis. By integrating physical constraints, the proposed method improves predictive performance while maintaining a lower parameter count, making it relevant for both academic research and practical applications in AI and machine learning.
Key Takeaways
- Introduces a physical-guided neural network for improved spatiotemporal prediction.
- Utilizes an adaptive second-order Runge-Kutta method to model physical states accurately.
- Incorporates a frequency-enhanced Fourier module to boost dynamic estimation capabilities.
- Demonstrates superior performance over state-of-the-art methods with fewer parameters.
- Applicable in various fields including weather forecasting and human action recognition.
Computer Science > Computer Vision and Pattern Recognition arXiv:2405.14504 (cs) [Submitted on 23 May 2024 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Adaptive Runge-Kutta Dynamics for Spatiotemporal Prediction Authors:Xuanle Zhao, Yue Sun, Ziyi Wang, Bo Xu, Tielin Zhang View a PDF of the paper titled Adaptive Runge-Kutta Dynamics for Spatiotemporal Prediction, by Xuanle Zhao and 3 other authors View PDF HTML (experimental) Abstract:Spatiotemporal prediction is important in solving natural problems and processing video frames, especially in weather forecasting and human action recognition. Recent advances attempt to incorporate prior physical knowledge into the deep learning framework to estimate the unknown governing partial differential equations (PDEs) in complex dynamics, which have shown promising results in spatiotemporal prediction tasks. However, previous approaches only restrict neural network architectures or loss functions to acquire physical or PDE features, which decreases the representative capacity of a neural network. Meanwhile, the updating process of the physical state cannot be effectively estimated. To solve the problems mentioned above, we introduce a physical-guided neural network, which utilizes an adaptive second-order Runge-Kutta method with physical constraints to model the physical states more precisely. Furthermore, we propose a frequency-enhanced Fourier module to strengthen the model's ability to estimate the spatiotemporal dynami...
