  This paper extends a low-rank tensor decomposition (LRTD) reduced order model
(ROM) methodology to simulate viscous flows and in particular to predict a
smooth branch of solutions for the incompressible Navier-Stokes equations.
Additionally, it enhances the LRTD-ROM methodology by introducing a
non-interpolatory variant, which demonstrates improved accuracy compared to the
interpolatory method utilized in previous LRTD-ROM studies. After presenting
both the interpolatory and non-interpolatory LRTD-ROM, we demonstrate that with
snapshots from a few different viscosities, the proposed method is able to
accurately predict flow statistics in the Reynolds number range $[25,400]$.
This is a significantly wider and higher range than state of the art (and
similar size) ROMs built for use on varying Reynolds number have been
successful on. The paper also discusses how LRTD may offer new insights into
the properties of parametric solutions.
