Research
Core Machine Learning
Learning Neural Event Functions for Ordinary Differential Equations
May 3, 2021
Abstract
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.
AUTHORS
Written by
Ricky T. Q. Chen
Brandon Amos
Maximilian Nickel
Publisher
ICLR 2021
Research Topics
Core Machine Learning
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