Uncertainty and Structure in Neural Ordinary Differential Equations

Understand

Neural ordinary differential equations (ODEs) are an emerging class of deep learning models for dynamical systems.

• They are particularly useful for learning an ODE vector field from observed trajectories (i.e., inverse problems).
• We here consider aspects of these models relevant for their application in science and engineering.
• Scientific predictions generally require structured uncertainty estimates.