# Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework.

@inproceedings{Wu2018PhysicsInformedML, title={Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework.}, author={Jin-long Wu and Heng Xiao and Eric G. Paterson}, year={2018} }

- Published 2018
DOI:10.1103/PhysRevFluids.3.074602

Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncertainties in modeled Reynolds stresses. Recently, Wang et al. demonstrated that machine learning can be used to improve the RANS modeled Reynolds stresses by leveraging data from high fidelity simulations (Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS… CONTINUE READING

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