The democratization of machine learning requires architectures that automatically adapt to
new problems. Neural Differential Equations have emerged as a popular modeling …

Stiff systems of ordinary differential equations (ODEs) are pervasive in many science and
engineering fields, yet standard neural ODE approaches struggle to learn them. This …

Conventional notions of generalization often fail to describe the ability of learned models to
capture meaningful information from dynamical data. A neural network that learns complex …