• Corpus ID: 246275764

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

  title={Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models},
  author={Federico Fatone and Stefania Fresca and Andrea Manzoni},
Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs – built, e.g., exclusively through proper orthogonal decomposition (POD) – when applied to nonlinear time-dependent parametrized PDEs. In particular, POD-DL-ROMs can achieve extreme efficiency in the training stage and faster than real-time performances at testing, thanks to a prior dimensionality reduction through POD and a DL-based prediction framework… 


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