On Second Order Behaviour in Augmented Neural ODEs

Understand

Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures.

• The continuous nature of NODEs has made them particularly suitable for learning the dynamics of complex physical systems.
• While previous work has mostly been focused on first order ODEs, the dynamics of many systems, especially in classical physics, are governed by second order laws.
• In this work, we consider Second Order Neural ODEs (SONODEs).