Machine learning strategies for systems with invariance properties

@article{Ling2016MachineLS,
  title={Machine learning strategies for systems with invariance properties},
  author={Julia Ling and Reese E. Jones and Jeremy A. Templeton},
  journal={J. Comput. Physics},
  year={2016},
  volume={318},
  pages={22-35}
}
In many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds Averaged Navier Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition… CONTINUE READING

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