  Reduced-order modeling has a long tradition in computational fluid dynamics.
The ever-increasing significance of data for the synthesis of low-order models
is well reflected in the recent successes of data-driven approaches such as
Dynamic Mode Decomposition and Operator Inference. With this work, we suggest a
new approach to learning structured low-order models for incompressible flow
from data that can be used for engineering studies such as control,
optimization, and simulation. To that end, we utilize the intrinsic structure
of the Navier-Stokes equations for incompressible flows and show that learning
dynamics of the velocity and pressure can be decoupled, thus leading to an
efficient operator inference approach for learning the underlying dynamics of
incompressible flows. Furthermore, we show the operator inference performance
in learning low-order models using two benchmark problems and compare with an
intrusive method, namely proper orthogonal decomposition, and other data-driven
approaches.
