Julia Ling

4.2k total citations · 3 hit papers
60 papers, 2.9k citations indexed

About

Julia Ling is a scholar working on Computational Mechanics, Aerospace Engineering and Mechanical Engineering. According to data from OpenAlex, Julia Ling has authored 60 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Computational Mechanics, 26 papers in Aerospace Engineering and 26 papers in Mechanical Engineering. Recurrent topics in Julia Ling's work include Fluid Dynamics and Turbulent Flows (33 papers), Heat Transfer Mechanisms (21 papers) and Turbomachinery Performance and Optimization (18 papers). Julia Ling is often cited by papers focused on Fluid Dynamics and Turbulent Flows (33 papers), Heat Transfer Mechanisms (21 papers) and Turbomachinery Performance and Optimization (18 papers). Julia Ling collaborates with scholars based in United States, France and China. Julia Ling's co-authors include Jeremy Alan Templeton, Andrew Kurzawski, John K. Eaton, Reese E. Jones, Erin Antono, Christopher J. Elkins, Bryce Meredig, Julien Bodart, Maxwell Hutchinson and Ipek G. Kulahci and has published in prestigious journals such as The Journal of Chemical Physics, Chemistry of Materials and Journal of Fluid Mechanics.

In The Last Decade

Julia Ling

56 papers receiving 2.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

2016 1.1k citations Julia Ling, Jeremy Alan Templeton et al. profile →
Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

2015 288 citations Julia Ling, Jeremy Alan Templeton Physics of Fluids profile →
Machine learning strategies for systems with invariance properties

2016 263 citations Julia Ling, Jeremy Alan Templeton et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Julia Ling United States	23	1.7k	1.2k	911	649	337	60	2.9k
Jeremy Alan Templeton United States	15	1.3k 0.8×	1.0k 0.9×	531 0.6×	340 0.5×	244 0.7×	42	2.4k
Zhiping Mao United States	18	927 0.5×	1.6k 1.4×	436 0.5×	306 0.5×	187 0.6×	37	3.1k
Ameya D. Jagtap United States	15	1.3k 0.8×	2.6k 2.2×	618 0.7×	450 0.7×	154 0.5×	30	3.5k
Xuhui Meng China	18	862 0.5×	1.7k 1.5×	462 0.5×	315 0.5×	150 0.4×	35	2.8k
Koji Fukagata Japan	31	3.5k 2.1×	1.2k 1.0×	1.4k 1.5×	967 1.5×	81 0.2×	155	4.4k
Zhicheng Wang China	9	533 0.3×	1.1k 0.9×	318 0.3×	301 0.5×	126 0.4×	25	2.0k
Dunhui Xiao China	29	773 0.5×	1.1k 0.9×	338 0.4×	202 0.3×	81 0.2×	68	2.2k
Taku Nonomura Japan	35	3.3k 2.0×	328 0.3×	2.8k 3.0×	295 0.5×	75 0.2×	325	4.7k
Ehsan Haghighat United States	19	400 0.2×	1.0k 0.9×	208 0.2×	426 0.7×	138 0.4×	41	2.1k
Christophe Pierre United States	48	537 0.3×	457 0.4×	1.3k 1.4×	1.8k 2.8×	150 0.4×	269	8.1k

Countries citing papers authored by Julia Ling

Since Specialization

Citations

This map shows the geographic impact of Julia Ling's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julia Ling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia Ling more than expected).

Fields of papers citing papers by Julia Ling

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Julia Ling. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julia Ling. The network helps show where Julia Ling may publish in the future.

Co-authorship network of co-authors of Julia Ling

This figure shows the co-authorship network connecting the top 25 collaborators of Julia Ling. A scholar is included among the top collaborators of Julia Ling based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Julia Ling. Julia Ling is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chen, Yucong, et al.. (2025). Wide-Temperature Operational P2-Type Cathode via Protective Coating: Synergistic Air Stability Improvement and Mn Dissolution Mitigation. ACS Applied Materials & Interfaces. 17(38). 53512–53525.

Ling, Julia, et al.. (2025). High-Entropy Strategy Enables Low Volume Strain and High Energy Density through Suppressing the Phase Separation in NASICON Cathodes. Chemistry of Materials. 37(22). 9308–9318.

Chen, Yucong, Yuze Huang, Julia Ling, et al.. (2025). Diphosphate coating of MnPO4/Na3PO4 enhances interfacial stability of P2-Na0.67Mn0.95Mg0.05O2 cathode in sodium-ion batteries. Electrochimica Acta. 546. 147847–147847.

Hegde, Vinay I., Christopher K. H. Borg, Maxwell Hutchinson, et al.. (2023). Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD. Physical Review Materials. 7(5). 20 indexed citations

Ling, Julia, et al.. (2021). On the generality of tensor basis neural networks for turbulent scalar flux modeling. International Communications in Heat and Mass Transfer. 128. 105626–105626. 16 indexed citations

Antono, Erin, Nobuyuki Matsuzawa, Julia Ling, et al.. (2020). Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials. The Journal of Physical Chemistry A. 124(40). 8330–8340. 30 indexed citations

Kim, Edward, et al.. (2020). Machine-learned metrics for predicting the likelihood of success in materials discovery. npj Computational Materials. 6(1). 24 indexed citations

Rupp, Matthias, et al.. (2020). Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization. The Journal of Chemical Physics. 153(2). 24112–24112. 34 indexed citations

Hutchinson, Maxwell, Erin Antono, Brenna M. Gibbons, et al.. (2018). Solving industrial materials problems by using machine learning across diverse computational and experimental data. Bulletin of the American Physical Society. 2018. 4 indexed citations

10.

Ray, Jaideep, et al.. (2018). Robust Bayesian Calibration of a k−ε Model for Compressible Jet-in-Crossflow Simulations. AIAA Journal. 56(12). 4893–4909. 23 indexed citations

11.

Ling, Julia, et al.. (2018). Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows. 17 indexed citations

12.

Weatheritt, Jack, et al.. (2017). A Comparative Study of Contrasting Machine Learning Frameworks Applied to RANS Modeling of Jets in Crossflow. Open Archive Toulouse Archive Ouverte (University of Toulouse). 25 indexed citations

13.

Ling, Julia & Jeremy Alan Templeton. (2017). Tensor Basis Neural Network v. 1.0 (beta). OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations

14.

Wu, Jinlong, Jianxun Wang, Heng Xiao, & Julia Ling. (2017). Visualization of High Dimensional Turbulence Simulation Data using t-SNE. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 22 indexed citations

15.

Wu, Jinlong, Jianxun Wang, Heng Xiao, & Julia Ling. (2016). Physics-Informed Machine Learning for Predictive Turbulence Modeling: A Priori Assessment of Prediction Confidence. arXiv (Cornell University). 10 indexed citations

16.

Ling, Julia & Jeremy Alan Templeton. (2015). Machine Learning Models for Detection of Regions of High Model Form Uncertainty in RANS.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations

17.

Ling, Julia & Jeremy Alan Templeton. (2015). Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty. Physics of Fluids. 27(8). 288 indexed citations breakdown →

18.

Ling, Julia, Christopher J. Elkins, & John K. Eaton. (2014). Optimal Turbulent Schmidt Number for RANS Modeling of Trailing Edge Slot Film Cooling. 3 indexed citations

19.

Ling, Julia, Julien Bodart, Filippo Coletti, & John Eaton. (2013). K-Means Clustering for Data Visualization and Flow Interpretation: Inclined Jet in Crossflow Example. Bulletin of the American Physical Society. 1 indexed citations

20.

Ling, Julia, et al.. (2013). Experimentally informed optimization of turbulent diffusivity for a discrete hole film cooling geometry. International Journal of Heat and Fluid Flow. 44. 348–357. 15 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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