论文标题
拆开的多级变换网络，用于预测稀疏观察的时空动力学
Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
论文作者
论文摘要
在本文中，我们解决了预测复杂的非线性时空动力学的问题，当可用数据记录在不规则间隔的稀疏空间位置时。用于建模时空动力学的大多数现有深度学习模型都是为常规网格中的数据而设计的，或者难以从稀疏和不规则间隔的数据站点中发现空间关系。我们提出了一个深度学习模型，该模型学习使用稀疏分布的数据位点的数据来预测未知的时空动力学。我们以径向基础功能（RBF）搭配方法为基础，该方法通常用于偏微分方程（PDE）的无网格解决方案。 RBF框架使我们能够阐明观察到的时空函数，并学习RBF空间上数据位点之间的空间相互作用。然后，学习的空间特征被用来构成原始观测值的多级变换，并在未来的时间步骤中预测其演变。我们使用合成和现实世界的气候数据证明了方法的优势。
In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most of the existing deep learning models for modeling spatiotemporal dynamics are either designed for data in a regular grid or struggle to uncover the spatial relations from sparse and irregularly-spaced data sites. We propose a deep learning model that learns to predict unknown spatiotemporal dynamics using data from sparsely-distributed data sites. We base our approach on Radial Basis Function (RBF) collocation method which is often used for meshfree solution of partial differential equations (PDEs). The RBF framework allows us to unravel the observed spatiotemporal function and learn the spatial interactions among data sites on the RBF-space. The learned spatial features are then used to compose multilevel transformations of the raw observations and predict its evolution in future time steps. We demonstrate the advantage of our approach using both synthetic and real-world climate data.
