  We present a machine learning-based mesh refinement technique for steady and
unsteady flows. The clustering technique proposed by Otmani et al.
arXiv:2207.02929 [physics.flu-dyn] is used to mark the viscous and turbulent
regions for the flow past a cylinder at Re=40 (steady laminar flow) and Re=3900
(unsteady turbulent flow). Within this clustered region, we increase the
polynomial order to show that it is possible to obtain similar levels of
accuracy to a uniformly refined mesh. The method is effective as the clustering
successfully identifies the two flow regions, a viscous/turbulent dominated
region (including the boundary layer and wake) and an inviscid/irrotational
region (a potential flow region). The data used within this framework are
generated using a high-order discontinuous Galerkin solver, allowing to locally
refine the polynomial order (p-refinement) in each element of the clustered
region. For the steady laminar test case we are able to reduce the
computational cost up to 32% and for the unsteady turbulent case up to 33%.
