A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

@article{Champion2020AUS,
  title={A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data},
  author={Kathleen P. Champion and Peng Zheng and A. Aravkin and S. Brunton and J. Kutz},
  journal={IEEE Access},
  year={2020},
  volume={8},
  pages={169259-169271}
}
Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models that can (i) generalize to predict previously unobserved behaviors, (ii) provide effective forecasting predictions (extrapolation), and (iii) be certifiable. Autonomous systems will necessarily interact with changing and uncertain environments… Expand
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