Mark D. Boyer

1.5k total citations
58 papers, 733 citations indexed

About

Mark D. Boyer is a scholar working on Nuclear and High Energy Physics, Aerospace Engineering and Biomedical Engineering. According to data from OpenAlex, Mark D. Boyer has authored 58 papers receiving a total of 733 indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Nuclear and High Energy Physics, 27 papers in Aerospace Engineering and 18 papers in Biomedical Engineering. Recurrent topics in Mark D. Boyer's work include Magnetic confinement fusion research (49 papers), Fusion materials and technologies (18 papers) and Superconducting Materials and Applications (17 papers). Mark D. Boyer is often cited by papers focused on Magnetic confinement fusion research (49 papers), Fusion materials and technologies (18 papers) and Superconducting Materials and Applications (17 papers). Mark D. Boyer collaborates with scholars based in United States, China and South Korea. Mark D. Boyer's co-authors include Eugenio Schuster, Keith Erickson, David Humphreys, Robert D. Johnson, M.L. Walker, J.R. Ferron, B.G. Penaflor, Justin Barton, T. C. Luce and Egemen Kolemen and has published in prestigious journals such as Nature Communications, Review of Scientific Instruments and IEEE Transactions on Control Systems Technology.

In The Last Decade

Mark D. Boyer

55 papers receiving 689 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark D. Boyer United States 14 624 295 229 213 146 58 733
Zhengping Luo China 14 719 1.2× 253 0.9× 337 1.5× 290 1.4× 152 1.0× 97 789
M. Lennholm United Kingdom 19 822 1.3× 296 1.0× 269 1.2× 352 1.7× 198 1.4× 75 887
P.C. de Vries Germany 15 676 1.1× 169 0.6× 168 0.7× 276 1.3× 250 1.7× 22 795
M. Baruzzo Italy 16 543 0.9× 173 0.6× 168 0.7× 202 0.9× 210 1.4× 49 626
M. Johnson United Kingdom 10 571 0.9× 166 0.6× 134 0.6× 207 1.0× 202 1.4× 18 706
P. Moreau France 17 616 1.0× 196 0.7× 203 0.9× 281 1.3× 211 1.4× 93 886
B.G. Penaflor United States 15 823 1.3× 342 1.2× 404 1.8× 284 1.3× 186 1.3× 65 868
Daniel Lewis Humphreys United States 15 1.0k 1.6× 336 1.1× 419 1.8× 390 1.8× 319 2.2× 36 1.1k
S. Brémond France 15 583 0.9× 310 1.1× 205 0.9× 202 0.9× 87 0.6× 65 755
F. Rimini United Kingdom 17 730 1.2× 193 0.7× 210 0.9× 489 2.3× 144 1.0× 41 867

Countries citing papers authored by Mark D. Boyer

Since Specialization
Citations

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

Fields of papers citing papers by Mark D. Boyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark D. Boyer. 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 Mark D. Boyer. The network helps show where Mark D. Boyer may publish in the future.

Co-authorship network of co-authors of Mark D. Boyer

This figure shows the co-authorship network connecting the top 25 collaborators of Mark D. Boyer. A scholar is included among the top collaborators of Mark D. Boyer 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 Mark D. Boyer. Mark D. Boyer 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.
Pau, A., Cristina Rea, O. Sauter, et al.. (2025). Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV. Nature Communications. 16(1). 8877–8877.
2.
Boyer, Mark D., et al.. (2024). Reversed magnetic shear scenario development in NSTX-U using TRANSP. Nuclear Fusion. 65(2). 26035–26035. 1 indexed citations
3.
Boyer, Mark D., et al.. (2024). Neural networks for estimation of divertor conditions in DIII-D using C III imaging. Nuclear Fusion. 64(10). 106056–106056. 2 indexed citations
4.
Munaretto, S., N.C. Logan, Zhirui Wang, et al.. (2023). Real time detection of multiple stable MHD eigenmode growth rates towards kink/tearing modes avoidance in DIII-D tokamak plasmas. Nuclear Fusion. 64(1). 16025–16025. 2 indexed citations
5.
Rafiq, T., et al.. (2023). Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations. Fusion Engineering and Design. 191. 113514–113514. 5 indexed citations
6.
Barr, J.L., B. Sammuli, David Humphreys, et al.. (2021). Development and experimental qualification of novel disruption prevention techniques on DIII-D. Nuclear Fusion. 61(12). 126019–126019. 21 indexed citations
7.
Boyer, Mark D., et al.. (2021). Prediction of electron density and pressure profile shapes on NSTX-U using neural networks. Nuclear Fusion. 61(4). 46024–46024. 10 indexed citations
8.
Rafiq, T., et al.. (2020). Control-Oriented Current-Profile Response Modeling Using Neural Network Accelerated Versions of TGLF and NUBEAM for DIII-D. APS Division of Plasma Physics Meeting Abstracts. 2020. 1 indexed citations
9.
Mehta, Viraj, Willie Neiswanger, A. Nelson, et al.. (2020). Neural Dynamical Systems. International Conference on Learning Representations. 2 indexed citations
10.
Boyer, Mark D., et al.. (2020). Accelerated version of NUBEAM capabilities in DIII-D using neural networks. Fusion Engineering and Design. 163. 112125–112125. 13 indexed citations
11.
Humphreys, David, Mark D. Boyer, J.M. Canik, et al.. (2020). Advancing Fusion with Machine Learning Research Needs Workshop Report. Journal of Fusion Energy. 39(4). 123–155. 22 indexed citations
12.
Mueller, D., S.H. Hahn, N.W. Eidietis, et al.. (2019). Improved fast vertical control in KSTAR. Fusion Engineering and Design. 141. 9–14. 10 indexed citations
13.
Boyer, Mark D., et al.. (2019). Mode rotation control in a tokamak with a feedback-driven biased electrode. Review of Scientific Instruments. 90(2). 23503–23503. 4 indexed citations
14.
Neiswanger, Willie, Kirthevasan Kandasamy, A. Nelson, et al.. (2019). Offline Contextual Bayesian Optimization. Neural Information Processing Systems. 32. 4627–4638. 6 indexed citations
15.
Montes, Kevin, Cristina Rea, R. Granetz, et al.. (2019). Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST. Nuclear Fusion. 59(9). 96015–96015. 77 indexed citations
16.
Gerhardt, S. P., et al.. (2018). Forensic Analysis of Faulted NSTX-U Inner Poloidal Field Coil. IEEE Transactions on Plasma Science. 46(7). 2653–2662. 10 indexed citations
17.
Boyer, Mark D., et al.. (2018). Design and simulation of the snowflake divertor control for NSTX–U. Plasma Physics and Controlled Fusion. 61(3). 35005–35005. 6 indexed citations
18.
Scoville, J. T., et al.. (2018). New capabilities and upgrade path for the DIII-D neutral beam heating system. Fusion Engineering and Design. 146. 6–9. 13 indexed citations
19.
Barton, Justin, Mark D. Boyer, Eugenio Schuster, et al.. (2012). Toroidal current profile control during low confinement mode plasma discharges in DIII-D via first-principles-driven model-based robust control synthesis. Nuclear Fusion. 52(12). 123018–123018. 45 indexed citations
20.
Salino, P. A., et al.. (2003). How Modern Engine Oils can impact on Emission Reduction. SAE technical papers on CD-ROM/SAE technical paper series. 1. 2 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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