Corpus ID: 236428901

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques

@article{Kadeethum2021NonintrusiveRO,
  title={Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques},
  author={T. Kadeethum and Francesco Ballarin and Y. Cho and Daniel O'Malley and H. Yoon and Nikolaos Bouklas},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11460}
}
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO2 sequestration). Here, we present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the… Expand

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