Corpus ID: 123921337

Galerkin v. discrete-optimal projection in nonlinear model reduction

@article{Carlberg2015GalerkinVD,
  title={Galerkin v. discrete-optimal projection in nonlinear model reduction},
  author={Kevin T. Carlberg and Matthew F. Barone and Harbir Antil},
  journal={ArXiv},
  year={2015},
  volume={abs/1504.03749}
}
Discrete-optimal model-reduction techniques such as the Gauss--Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible flow problems where standard Galerkin techniques have failed. However, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform projection at the time-continuous level, while… Expand
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