• Corpus ID: 237592730

An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

@article{Pichi2021AnAN,
  title={An artificial neural network approach to bifurcating phenomena in computational fluid dynamics},
  author={Federico Pichi and Francesco Ballarin and Gianluigi Rozza and Jan S. Hesthaven},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.10765}
}
This 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 NavierStokes 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… 
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