Pichi, FedericoBallarin, FrancescoRozza, GianluigiHesthaven, Jan S.2023-03-272023-03-272023-03-272023-02-0810.1016/j.compfluid.2023.105813https://infoscience.epfl.ch/handle/20.500.14299/196503WOS:000933994400001This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks. We discuss the POD-NN approach dealing with non-smooth solutions set of nonlinear parametrized PDEs. Thus, we study the Navier-Stokes equations describing: (i) the Coanda effect in a channel, and (ii) the lid driven triangular cavity flow, in a physical/geometrical multi-parametrized setting, considering the effects of the domain's configuration on the position of the bifurcation points. Finally, we propose a reduced manifold-based bifurcation diagram for a non-intrusive recovery of the critical points evolution. Exploiting such detection tool, we are able to efficiently obtain information about the pattern flow behaviour, from symmetry breaking profiles to attaching/spreading vortices, even in the advection-dominated regime.Computer Science, Interdisciplinary ApplicationsMechanicsComputer ScienceMechanicsreduced order modellingartificial neural networkbifurcation analysiscomputational fluid dynamicsnavier-stokes equationsempirical interpolationnonlinear problemsmodel-reductionflowstext::journal::journal article::research article