Physics-Informed Machine Learning for Predictive Turbulence Modeling: Using Data to Improve RANS Modeled Reynolds Stresses

By Jian-Xun Wang, Jin-Long Wu, H. Xiao. 2016

30 citations recorded by the source corpus.

How researchers see this paper

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1. Anuj Karpatne
2. Gowtham Atluri
3. Vipin Kumar
4. Michael S. Steinbach
5. Arindam Banerjee
6. Anand Pratap Singh
7. James H. Faghmous
8. Shivaji Medida

Citations in the corpus

• Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, methodology): “bulence modeling efforts [20], [21], discovery of novel compounds in material science [22], [23], [24], design of density functionals in quantum chemistry [25], improved imaging”
• Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils (2016, methodology): “Finally, respecting realizability limits [18, 30] and invariance properties [64] will be necessary to constrain the model, especially when the model is operating in an extrapolatory mode.”