Corpus ID: 235458057

Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators

@article{Bai2021NonintrusiveNM,
  title={Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators},
  author={Zhe Bai and Liqian Peng},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09658}
}
Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a nonintrusive manner… Expand

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