Jonathan F. MacArt

524 total citations
29 papers, 339 citations indexed

About

Jonathan F. MacArt is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Aerospace Engineering. According to data from OpenAlex, Jonathan F. MacArt has authored 29 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computational Mechanics, 9 papers in Statistical and Nonlinear Physics and 8 papers in Aerospace Engineering. Recurrent topics in Jonathan F. MacArt's work include Fluid Dynamics and Turbulent Flows (17 papers), Combustion and flame dynamics (13 papers) and Model Reduction and Neural Networks (9 papers). Jonathan F. MacArt is often cited by papers focused on Fluid Dynamics and Turbulent Flows (17 papers), Combustion and flame dynamics (13 papers) and Model Reduction and Neural Networks (9 papers). Jonathan F. MacArt collaborates with scholars based in United States, United Kingdom and Germany. Jonathan F. MacArt's co-authors include Michael E. Mueller, Justin Sirignano, Jonathan B. Freund, Temistocle Grenga, Justin Sirignano, Jonathan M. Wang, Konstantinos Spiliopoulos, Marco Panesi, Pavel P. Popov and Heinz Pitsch and has published in prestigious journals such as Journal of Fluid Mechanics, Journal of Computational Physics and AIAA Journal.

In The Last Decade

Jonathan F. MacArt

24 papers receiving 333 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonathan F. MacArt United States 11 288 111 82 74 57 29 339
Guido Lodato France 14 620 2.2× 111 1.0× 34 0.4× 228 3.1× 67 1.2× 34 689
Guillaume Lehnasch France 14 413 1.4× 51 0.5× 58 0.7× 215 2.9× 39 0.7× 33 464
Alessandro Orchini Germany 14 437 1.5× 267 2.4× 50 0.6× 138 1.9× 114 2.0× 54 495
Riccardo Malpica Galassi Italy 15 425 1.5× 324 2.9× 31 0.4× 162 2.2× 23 0.4× 49 509
Georg A. Mensah Germany 13 361 1.3× 201 1.8× 36 0.4× 109 1.5× 135 2.4× 24 397
Bruce A. Perry United States 9 221 0.8× 138 1.2× 14 0.2× 86 1.2× 21 0.4× 22 298
J. S. Kim United States 12 387 1.3× 260 2.3× 31 0.4× 161 2.2× 33 0.6× 16 531
Pavel P. Popov United States 12 321 1.1× 159 1.4× 9 0.1× 110 1.5× 38 0.7× 24 366
Tongxun Yi United States 12 512 1.8× 251 2.3× 20 0.2× 155 2.1× 60 1.1× 41 562
Matthew E. Harvazinski United States 12 500 1.7× 269 2.4× 52 0.6× 309 4.2× 45 0.8× 48 558

Countries citing papers authored by Jonathan F. MacArt

Since Specialization
Citations

This map shows the geographic impact of Jonathan F. MacArt'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 Jonathan F. MacArt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan F. MacArt more than expected).

Fields of papers citing papers by Jonathan F. MacArt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan F. MacArt. 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 Jonathan F. MacArt. The network helps show where Jonathan F. MacArt may publish in the future.

Co-authorship network of co-authors of Jonathan F. MacArt

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan F. MacArt. A scholar is included among the top collaborators of Jonathan F. MacArt 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 Jonathan F. MacArt. Jonathan F. MacArt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
MacArt, Jonathan F., et al.. (2025). Online optimisation of machine learning collision models to accelerate direct molecular simulation of rarefied gas flows. Journal of Computational Physics. 549. 114601–114601.
2.
3.
Pal, Pinaki, et al.. (2025). Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems. Physica D Nonlinear Phenomena. 476. 134650–134650. 3 indexed citations
4.
MacArt, Jonathan F., et al.. (2025). Neural network-augmented eddy viscosity closures for turbulent premixed jet flames. Combustion and Flame. 278. 114241–114241.
5.
MacArt, Jonathan F., et al.. (2025). Active Control of Turbulent Airfoil Flows Using Adjoint-Based Deep Learning. 1 indexed citations
6.
Kryger, M. H. & Jonathan F. MacArt. (2025). Optimization of Second-Order Transport Models for Transition-Continuum Flows. AIAA Journal. 63(10). 4223–4233.
7.
MacArt, Jonathan F., et al.. (2024). A TVD neural network closure and application to turbulent combustion. Journal of Computational Physics. 523. 113638–113638. 1 indexed citations
8.
Singh, Narendra, et al.. (2024). Physics-constrained deep learning-based model for non-equilibrium flows. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1 indexed citations
9.
Petkov, P., В. Н. Павлов, Temistocle Grenga, et al.. (2024). Parallel implementation and performance of super-resolution generative adversarial network turbulence models for large-eddy simulation. Computers & Fluids. 288. 106498–106498. 4 indexed citations
10.
11.
Pitsch, Heinz, et al.. (2024). Influence of adversarial training on super-resolution turbulence reconstruction. Physical Review Fluids. 9(6). 10 indexed citations
12.
Sirignano, Justin, et al.. (2023). Deep Learning Closure of the Navier–Stokes Equations for Transition-Continuum Flows. AIAA Journal. 61(12). 5484–5497. 8 indexed citations
13.
Sirignano, Justin & Jonathan F. MacArt. (2023). Deep learning closure models for large-eddy simulation of flows around bluff bodies. Journal of Fluid Mechanics. 966. 20 indexed citations
14.
MacArt, Jonathan F., Justin Sirignano, & Marco Panesi. (2022). Deep Learning Closure of the Navier--Stokes Equations for Transitional Flows. AIAA SCITECH 2022 Forum. 2 indexed citations
15.
Sirignano, Justin, Jonathan F. MacArt, & Jonathan B. Freund. (2020). Embedded training of neural-network sub-grid-scale turbulence models. Bulletin of the American Physical Society. 2 indexed citations
16.
Popov, Pavel P., Munetake Nishihara, Alessandro Munafò, et al.. (2020). Laser-Induced Breakdown Ignition of Low-Pressure Hydrogen-Air Premixtures. AIAA Scitech 2020 Forum. 1 indexed citations
17.
MacArt, Jonathan F., et al.. (2020). Heat release effects on the Reynolds stress budgets in turbulent premixed jet flames at low and high Karlovitz numbers. Combustion and Flame. 216. 1–8. 16 indexed citations
18.
Perry, Bruce A., et al.. (2019). Data-Driven Dimension Reduction in Turbulent Combustion: Utility and Limitations. AIAA Scitech 2019 Forum. 2 indexed citations
19.
MacArt, Jonathan F.. (2018). Computational Simulation and Modeling of Heat Release Effects on Turbulence in Turbulent Reacting Flow. PhDT. 1 indexed citations
20.
MacArt, Jonathan F., Temistocle Grenga, & Michael E. Mueller. (2018). Evolution of flame-conditioned velocity statistics in turbulent premixed jet flames at low and high Karlovitz numbers. Proceedings of the Combustion Institute. 37(2). 2503–2510. 21 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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