Publications

Ling, J. (2016). Using Tensor Theory to Embed Invariances: A Case Study from Turbulence Modeling [Conference Poster]. https://www.osti.gov/biblio/1410772

Ling, J., Barone, M.F., Davis, W.L., Chowdhary, K., & Fike, J. (2016). Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations [Conference Poster]. 10.2514/6.2017-0755

Wu, J., Wang, J., Xiao, H., & Ling, J. (2016). Visualization of High Dimensional Turbulence Simulation Data using t-SNE [Conference Poster]. https://www.osti.gov/biblio/1422130

Dechant, L., Ray, J., Lefantzi, S., Ling, J., & Arunajatesan, S. (2016). K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution [Conference Poster]. https://www.osti.gov/biblio/1415716

Weatheritt, J., Sandberg, R., Ling, J., Bodart, J., & Saez, G. (2016). A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow [Conference Poster]. 10.1115/GT2017-63403

Ling, J. (2016). Machine Learning for Turbulence Modeling [Presentation]. https://www.osti.gov/biblio/1404810

Milani, P., Ling, J., & Eaton, J. (2016). A Machine Learning Approach for Modeling Turbulent Scalar Flux in Jet in Crossflow [Presentation]. https://www.osti.gov/biblio/1406875

Ling, J. (2016). Machine Learning for Turbulence Modeling [Presentation]. https://www.osti.gov/biblio/1404809

Wang, J., Wu, J., Ling, J., Iaccarino, G., & Xiao, H. (2016). Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. 10.2172/1562229

Ling, J. (2016). Machine Learning for Turbulence Modeling [Presentation]. https://www.osti.gov/biblio/1380200

Ling, J. (2016). Using Tensor Theory to Embed Invariances: A Case Study from Turbulence Modeling [Conference Poster]. https://www.osti.gov/biblio/1581546

Ling, J., Jones, R.E., & Templeton, J.A. (2016). Machine learning strategies for systems with invariance properties. Journal of Computational Physics, 318(C), pp. 22-35. 10.1016/j.jcp.2016.05.003

Kelkar, O., & Ling, J. (2016). Python Machine Learning Interface for Turbulence Simulations [Presentation]. https://www.osti.gov/biblio/1373056

Kury, M., & Ling, J. (2016). Machine Learning Extension for Fuego [Presentation]. https://www.osti.gov/biblio/1373068

Ling, J., & Templeton, J.A. (2016). Machine Learning and Turbulent Flows: Learning from Turbulence Simulation Data Sets [Presentation]. https://www.osti.gov/biblio/1368896

Barone, M.F., Ling, J., & Davis, W.L. (2016). Development of Machine Learning Models for Turbulent Wall Pressure Fluctuations [Conference Poster]. https://www.osti.gov/biblio/1368710

Ling, J. (2016). Using machine learning to understand and mitigate model form uncertainty in turbulence models [Conference Poster]. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. 10.1109/ICMLA.2015.38

Ling, J. (2016). Using Machine Learning to Detect and Reduce Uncertainty in Turbulent Flow Simulations [Presentation]. https://www.osti.gov/biblio/1344702

Ling, J., Ruiz, A., Lacaze, G., & Oefelein, J. (2016). Uncertainty analysis and data-driven model advances for a jet-in-crossflow [Conference Poster]. Proceedings of the ASME Turbo Expo. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84992755523&origin=inward

Ling, J., & Templeton, J.A. (2015). Machine Learning for Uncertainty Quantification in Turbulent Flow Simulations [Conference Poster]. https://www.osti.gov/biblio/1338869

Ling, J. (2015). Using Machine Learning for Error Detection in Turbulent Flow Simulations [Presentation]. https://www.osti.gov/biblio/1332857

Ling, J. (2015). Using Machine Learning to Understand and Mitigate [Conference Poster]. https://www.osti.gov/biblio/1332856

Ling, J., & Templeton, J.A. (2015). Machine Learning Models for Detection of Regions of High Model Form Uncertainty in RANS [Conference Poster]. https://www.osti.gov/biblio/1331933

Ling, J. (2015). Using Machine Learning to Detect Regions of High Model-form Uncertainty in Turbulence Simulations [Presentation]. https://www.osti.gov/biblio/1531111

Templeton, J.A., Blaylock, M.L., Domino, S.P., Hewson, J.C., Kumar, P.R., Ling, J., Najm, H.N., Ruiz, A., Safta, C., Sargsyan, K., Stewart, A., & Wagner, G. (2015). Calibration and Forward Uncertainty Propagation for Large-eddy Simulations of Engineering Flows. 10.2172/1221181