Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization

@article{Baddoo2021KernelLF,
  title={Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization},
  author={Peter J. Baddoo and Benjamin Herrmann and Beverley J. McKeon and Steven L. Brunton},
  journal={Proceedings. Mathematical, Physical, and Engineering Sciences},
  year={2021},
  volume={478}
}
Research in modern data-driven dynamical systems is typically focused on the three key challenges of high dimensionality, unknown dynamics and nonlinearity. The dynamic mode decomposition (DMD) has emerged as a cornerstone for modelling high-dimensional systems from data. However, the quality of the linear DMD model is known to be fragile with respect to strong nonlinearity, which contaminates the model estimate. By contrast, sparse identification of nonlinear dynamics learns fully nonlinear… 

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