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.
翻译:现有的神经元数据交换器的配制取决于对终止时间的明确了解。我们将神经元数据交换机扩展至以神经事件功能为模型的隐含定义的终止标准,这些功能可以连锁并有区别。神经事件数据交换机可以模拟连续时间系统中的离散和瞬时变化,而事先不知道这些变化何时发生,或应存在多少这种变化。我们测试了我们模拟混合离散和连续系统的方法,例如转换动态系统和多机体系统中的碰撞,我们提议对有离散控制应用的点程序进行模拟培训。