论文标题
物理成立的卷积复发性神经网络，用于源识别和预测动态系统
Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems
论文作者
论文摘要
物理过程的时空动力学通常是使用部分微分方程（PDE）建模的。尽管核心动态遵循物理学的一些原理，但实际的物理过程通常是由未知的外部来源驱动的。在这种情况下，开发纯粹的分析模型变得非常困难，并且数据驱动的建模可能会有所帮助。在本文中，我们提出了一个混合框架，将基于物理学的数值模型与深度学习结合了源识别和预测时空动力学系统的深度学习，并具有不可观察的时间变化的外部来源。我们将模型模型制定为卷积复发性神经网络（RNN），该神经网络（RNN）是可以端到端的，可用于动态系统的时空演化预测，并将源行为作为RNN的内部状态学习。实验结果表明，所提出的模型可以预测动力学相对较长的时间并确定来源。
Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs). Though the core dynamics follows some principles of physics, real-world physical processes are often driven by unknown external sources. In such cases, developing a purely analytical model becomes very difficult and data-driven modeling can be of assistance. In this paper, we present a hybrid framework combining physics-based numerical models with deep learning for source identification and forecasting of spatio-temporal dynamical systems with unobservable time-varying external sources. We formulate our model PhICNet as a convolutional recurrent neural network (RNN) which is end-to-end trainable for spatio-temporal evolution prediction of dynamical systems and learns the source behavior as an internal state of the RNN. Experimental results show that the proposed model can forecast the dynamics for a relatively long time and identify the sources as well.
