Using Machine Learning to Understand and Mitigate Model Form Uncertainty in Turbulence Models. - Julia Ling (ICMLA, 2015)

Machine learning strategies for systems with invariance properties. - Julia Ling; Reese E. Jones; Jeremy A. Templeton (J. Comput. Phys., 2016)

Using feature importance metrics to detect events of interest in scientific computing applications. - Julia Ling; W. Philip Kegelmeyer; Aditya Konduri; Hemanth Kolla; Kevin A. Reed; Timothy M. Shead; Warren L. Davis IV (LDAV, 2017)

Building Data-driven Models with Microstructural Images: Generalization and Interpretability. - Julia Ling; Maxwell Hutchinson; Erin Antono; Brian DeCost; Elizabeth A. Holm; Bryce Meredig (CoRR, 2017)

Overcoming data scarcity with transfer learning. - Maxwell L. Hutchinson; Erin Antono; Brenna M. Gibbons; Sean Paradiso; Julia Ling; Bryce Meredig (CoRR, 2017)

Generalization of machine-learned turbulent heat flux models applied to film cooling flows. - Pedro M. Milani; Julia Ling; John K. Eaton (CoRR, 2019)

Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization. - Zachary del Rosario; Yoolhee Kim; Matthias Rupp; Erin Antono; Julia Ling (CoRR, 2019)

Machine-learned metrics for predicting the likelihood of success in materials discovery. - Yoolhee Kim; Edward Kim; Erin Antono; Bryce Meredig; Julia Ling (CoRR, 2019)

Anomaly detection in scientific data using joint statistical moments. - Aditya Konduri; Hemanth Kolla; W. Philip Kegelmeyer; Timothy M. Shead; Julia Ling; Warren L. Davis IV (J. Comput. Phys., 2019)

Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling. - Pedro M. Milani; Julia Ling; John K. Eaton (CoRR, 2020)