The basic idea to construct GRPCS is to establish object topology first, then use geometry to change the shape of differential manifold.
其基本思想是首先建立物体的拓扑信息，然后加载几何信息来构造曲面，整个构造过程类似于将与控制网格拓扑同胚的微分流形经控制顶点拉伸后进行弹性形变。
参考来源 - 流形上参数曲面的理论与方法·2,447,543篇论文数据，部分数据来源于NoteExpress
This algorithm avoids soling differential equation frequently as well as redundant orbit, and it is easy to control the accuracy and to get the picture of manifold.
该算法避免了频繁求解微分方程问题，没有求解多余的轨道，且求解的精度也容易控制，得到的流形图直观。
Research of the group of motions in Riemann manifold is an important question of the differential geometry.
黎曼流形运动群的研究是微分几何中一个重要问题。
In this paper, multi-layer feedforward neural networks are used to solve the nonlinear partial differential equation, and approach the centre manifold of the nonlinear system.
本文用多层前向神经网络求解该非线性偏微分方程，从而逼近非线性系统的中心流形。
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