ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting

• URL: http://arxiv.org/abs/2108.05940v1
• Date: Thu, 12 Aug 2021 19:34:00 GMT
• Title: ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
• Authors: Yu Huang, James Li, Min Shi, Hanqi Zhuang, Xingquan Zhu, Laurent Ch\'erubin, James VanZwieten, and Yufei Tang
• Abstract summary: We propose a physics-coupled neural network model to learn parameters governing the physics of the system. A-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals. Experiments, using simulated and field-collected ocean data, validate that ST-PCNN outperforms existing physics-informed models.
• Score: 15.265694039283106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ocean current, fluid mechanics, and many other spatio-temporal physical dynamical systems are essential components of the universe. One key characteristic of such systems is that certain physics laws -- represented as ordinary/partial differential equations (ODEs/PDEs) -- largely dominate the whole process, irrespective of time or location. Physics-informed learning has recently emerged to learn physics for accurate prediction, but they often lack a mechanism to leverage localized spatial and temporal correlation or rely on hard-coded physics parameters. In this paper, we advocate a physics-coupled neural network model to learn parameters governing the physics of the system, and further couple the learned physics to assist the learning of recurring dynamics. A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system. The physics-coupled learning ensures that the proposed model can be tremendously improved by using learned physics parameters, and can achieve good long-range forecasting (e.g., more than 30-steps). Experiments, using simulated and field-collected ocean current data, validate that ST-PCNN outperforms existing physics-informed models.

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