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Nonlinear proper orthogonal decomposition for convection-dominated flows

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Nonlinear Principal Component Analysis by Neural Networks: Theory and Application to the Lorenz System
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inferred← citeshttps://doi.org/10.1016/j.physd.2023.133711openalex-snapshot
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