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Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
[article]
2018
arXiv
pre-print
Model reduction of high-dimensional dynamical systems alleviates computational burdens faced in various tasks from design optimization to model predictive control. One popular model reduction approach is based on projecting the governing equations onto a subspace spanned by basis functions obtained from the compression of a dataset of solution snapshots. However, this method is intrusive since the projection requires access to the system operators. Further, some systems may require special
arXiv:1808.01346v2
fatcat:g46ttwgcdbfj5prd2l7f6lbzma