ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
This work addresses forecasting challenges in domains like oceanography and fluid mechanics by integrating learned physics into neural networks, representing an incremental improvement over prior physics-informed methods.
The paper tackles the problem of forecasting spatio-temporal physical dynamical systems like ocean currents by proposing ST-PCNN, a model that learns physics parameters and couples them with neural networks to improve long-range predictions, achieving better performance than existing physics-informed models in experiments with simulated and field-collected data.
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.