Publications

Aditya, K., Kolla, H., Ling, J., Kegelmeyer, W.P., Dunlavy, D.M., Shead, T.M., & Davis, W.L. (2018). EVENT DETECTION IN MULTI-VARIATE SCIENTIFIC SIMULATIONS USING FEATURE ANOMALY METRICS [Conference Poster]. https://www.osti.gov/biblio/1499084

Dechant, L., Ray, J., Lefantzi, S., Ling, J., & Arunajatesan, S. (2017). K-ε Turbulence Model Parameter Estimates Using an Approximate Self-similar Jet-in-Crossflow Solution. Journal of Propulsion and Power. 10.2514/6.2017-4167

Milani, P., Ling, J., & Eaton, J. (2017). A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows [Conference Poster]. https://www.osti.gov/biblio/1457104

Barone, M.F., Fike, J., Chowdhary, K., Davis, W.L., Ling, J., & Martin, S. (2017). Machine Learning Models of Errors in LES Predictions of Surface Pressure Fluctuations [Conference Poster]. https://www.osti.gov/biblio/1458163

Wilson, A., Wade, D., Ling, J., Chowdhary, K., Davis, W.L., Barone, M.F., & Fike, J. (2017). Convolutional Neural Networks for Frequency Response Predictions [Conference Poster]. https://www.osti.gov/biblio/1457870

Barone, M.F., Ling, J., Davis, W.L., Chowdhary, K., & Fike, J. (2017). Investigating Turbulent Wall Pressure Fluctuations using Machine Learning Techniques [Presentation]. https://www.osti.gov/biblio/1456663

Ling, J., & Kurzawski, A. (2017). Data-driven Adaptive Physics Modeling for Turbulence Simulations [Conference Poster]. https://doi.org/10.2514/6.2017-3627

Ling, J., Ruiz, A., Lacaze, G., & Oefelein, J. (2017). Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow [Conference Poster]. Journal of Turbomachinery. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84991669567&origin=inward

Ling, J. (2017). Machine Learning + Physics [Presentation]. https://www.osti.gov/biblio/1424568

Ling, J., & Templeton, J.A. (2017). Machine Learning for Turbulence Modeling. 10.2172/1761814

Ling, J. (2017). Machine Learning + Physics [Presentation]. https://www.osti.gov/biblio/1424881

Ling, J., Kegelmeyer, W.P., Aditya, K., Kolla, H., Reed, K., Shead, T.M., & Davis, W.L. (2017). Using Feature Importance Metrics to Detect Events of Interest in Scientific Computing Applications [Conference Poster]. 10.1109/LDAV.2017.8231851

Milani, P.M., Ling, J., Saez-Mischlich, G., Bodart, J., & Eaton, J.K. (2017). A machine learning approach for determining the turbulent diffusivity in film cooling flows [Conference Poster]. Proceedings of the ASME Turbo Expo. 10.1115/GT2017-63299

Dechant, L., Ray, J., Lefantzi, S., Ling, J., & Arunajatesan, S. (2017). K-ε turbulence model parameter estimates using an approximate self-similar jet-in-crossflow solution. 8th AIAA Theoretical Fluid Mechanics Conference, 2017. 10.2514/6.2017-4167

Ling, J., & Kurzawski, A. (2017). Data-driven adaptive physics modeling for turbulence simulations [Conference Poster]. 23rd AIAA Computational Fluid Dynamics Conference, 2017. 10.2514/6.2017-3627

Barone, M.F., Fike, J., Chowdhary, K., Davis, W.L., Ling, J., & Martin, S. (2017). Machine learning models of errors in large eddy simulation predictions of surface pressure fluctuations [Conference Poster]. 47th AIAA Fluid Dynamics Conference, 2017. 10.2514/6.2017-3979

Weatheritt, J., Sandberg, R.D., Ling, J., Saez, G., & Bodart, J. (2017). A comparative study of contrasting machine learning frameworks applied to rans modeling of jets in crossflow [Conference Poster]. Proceedings of the ASME Turbo Expo. 10.1115/GT2017-63403

Ling, J., Kurzawski, A., & Templeton, J.A. (2016). Deep Learning for Turbulence Modeling [Conference Poster]. https://www.osti.gov/biblio/1505474

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/1417545

Ling, J. (2016). Machine Learning for Turbulence Modeling: Embedding Invariance Properties [Presentation]. https://www.osti.gov/biblio/1416700

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

Banko, A., & Ling, J. (2016). Evaluating RANS Assumptions [Presentation]. https://www.osti.gov/biblio/1415216

Ling, J. (2016). Feature Importance [Conference Poster]. https://www.osti.gov/biblio/1415230

Ling, J., Kurzawski, A., & Templeton, J.A. (2016). Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics, 807, pp. 155-166. 10.1017/jfm.2016.615

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