# 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} }

- Published 2019
DOI:10.1016/j.ijthermalsci.2018.09.002

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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