Classification of machine learning frameworks for data-driven thermal fluid models

@inproceedings{Chang2019ClassificationOM,
  title={Classification of machine learning frameworks for data-driven thermal fluid models},
  author={Chih-Wei Chang and Nam Tran Dinh},
  year={2019}
}
Thermal fluid processes are inherently multi-physics and multi-scale, involving mass-momentum-energy transport phenomena. Thermal fluid simulation (TFS) is based on solving conservative equations, for which - except for"first-principle"direct numerical simulation - closure relations (CRs) are required to provide microscopic interactions. In practice, TFS is realized through reduced-order modeling, and its CRs can be informed by observations and data from relevant and adequately evaluated… CONTINUE READING
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