论文标题
学习普通微分方程的神经事件功能
Learning Neural Event Functions for Ordinary Differential Equations
论文作者
论文摘要
现有的神经颂歌配方依赖于终止时间的明确知识。我们将神经ODE扩展到隐式定义的终止标准，该标准由神经事件函数建立，可以将其链接在一起并通过分化。神经事件ODE能够在连续时间系统中对离散和瞬时变化进行建模，而无需事先了解这些变化何时会发生或应该存在多少此类更改。我们测试了建模混合离散和连续系统的方法，例如在多体系统中的切换动力学系统和碰撞，并提出了基于离散控制中应用的基于点过程的仿真培训。
The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous changes in a continuous-time system, without prior knowledge of when these changes should occur or how many such changes should exist. We test our approach in modeling hybrid discrete- and continuous- systems such as switching dynamical systems and collision in multi-body systems, and we propose simulation-based training of point processes with applications in discrete control.
