  Neural ordinary differential equations describe how values change in time.
This is the reason why they gained importance in modeling sequential data,
especially when the observations are made at irregular intervals. In this paper
we propose an alternative by directly modeling the solution curves - the flow
of an ODE - with a neural network. This immediately eliminates the need for
expensive numerical solvers while still maintaining the modeling capability of
neural ODEs. We propose several flow architectures suitable for different
applications by establishing precise conditions on when a function defines a
valid flow. Apart from computational efficiency, we also provide empirical
evidence of favorable generalization performance via applications in time
series modeling, forecasting, and density estimation.
