Machine learning adaptation for laminar and turbulent flows: applications to high order discontinuous Galerkin solvers
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This study proposes a machine learning-based method for mesh refinement in steady and unsteady flows. The regions to be refined are marked by using a clustering technique used to identify viscous and inviscid regions in a flow past a cylinder at different Reynolds numbers. After the regions have been marked, we apply $p$-refinement within these clustered regions to achieve a similar level of accuracy compared to uniform mesh while reducing the computational cost. The data used in this work have been generated using the high-order spectral element CFD solver HORSES3D.