GRAPH NEURAL NETWORKS REPRESENTING PHYSICAL SYSTEMS

A graph neural network system implementing a learnable physics engine for understanding and controlling a physical system. The physical system is considered to be composed of bodies coupled by jointsand is represented by static and dynamic graphs. A graph processing neural network processes an input graph e.g. the static and dynamic graphs, to provide an output graph, e.g. a predicted dynamic graph. The graph processing neural network is differentiable and may be used for control and/or reinforcement learning. The trained graph neural network system can be applied to physical systems with similar but new graph structures (zero-shot learning). 一种图神经网络系统，其实现用于理解和控制物理系统的可学习的物理引擎。所述物理系统被认为由通过关节耦合的主体组成并由静态图和动态图表示。图处理神经网络处理输入图，例如静态图和动态图，以提供输出图，例如预测的动态图。所述图处理神经网络是可微分的并且可以被用于控制和/或强化学习。经训练的图神经网络系统可以被应用于具有相似但新的图结构的物理系统(零样本学习)。