• Corpus ID: 218581737

Learning Data-Driven Stable Koopman Operators

@article{Mamakoukas2020LearningDS,
  title={Learning Data-Driven Stable Koopman Operators},
  author={Giorgos Mamakoukas and Ian Abraham and Todd D. Murphey},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.04291}
}
In this paper, we consider the problem of improving the long-term accuracy of data-driven approximations of Koopman operators, which are infinite-dimensional linear representations of general nonlinear systems, by bounding the eigenvalues of the linear operator. We derive a formula for the global error of general Koopman representations and motivate imposing stability constraints on the data-driven model to improve the approximation of nonlinear systems over a longer horizon. In addition… 
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TLDR
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