Mark D. Boyer

Boston Fusion (United States) — United States

Physics and Astronomy · Physical Sciences

20h-index1.4kcitations120works4.92yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Magnetic confinement fusion research(78), Superconducting Materials and Applications(30), Fusion materials and technologies(29), Particle accelerators and beam dynamics(27), Nuclear reactor physics and engineering(17)

Publications120 total

Axisymmetric n = 0 Growth Rate Modeling in Alcator C-Mod and SPARC: Rigid vs. Non-Rigid Body Approaches
Plasma Physics and Controlled Fusion·2026Open Access
Spherical tokamak physics research in preparation for the operation of NSTX-U
Nuclear Fusion·2026Open Access
Feedforward equilibrium trajectory optimization with GSPulse
Nuclear Fusion·2025Open Access
Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV
Nature Communications·2025· 1 citedOpen Access
Design and development of an off-normal warning system for SPARC
Nuclear Fusion·2025Open Access
Feedforward equilibrium trajectory optimization with GSPulse
arXiv (Cornell University)·2025Open Access
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
2025Open Access
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
arXiv (Cornell University)·2025Open Access
Reversed magnetic shear scenario development in NSTX-U using TRANSP
Nuclear Fusion·2024· 2 citedOpen Access
Neural networks for estimation of divertor conditions in DIII-D using C III imaging
Nuclear Fusion·2024· 4 citedOpen Access
DIII-D research to provide solutions for ITER and fusion energy
Nuclear Fusion·2024· 8 citedOpen Access
NSTX-U research advancing the physics of spherical tokamaks
Nuclear Fusion·2024· 43 citedOpen Access
Automated experimental design of safe rampdowns via probabilistic machine learning
Nuclear Fusion·2024· 6 citedOpen Access
Real time detection of multiple stable MHD eigenmode growth rates towards kink/tearing modes avoidance in DIII-D tokamak plasmas
Nuclear Fusion·2023· 4 citedOpen Access
Accelerating Fusion Energy with AI
WORLD SCIENTIFIC eBooks·2023· 1 cited
Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations
Fusion Engineering and Design·2023· 9 citedOpen Access
Exploration via Planning for Information about the Optimal Trajectory
arXiv (Cornell University)·2022Open Access
UNDERSTANDING X−…HOCl (X=Cl, Br, I) THROUGH VIBRATIONAL PERTURBATION THEORY
Proceedings of the 2022 International Symposium on Molecular Spectroscopy·2022Open Access
Neural net modeling of equilibria in NSTX-U
Nuclear Fusion·2022· 20 citedOpen Access
NSTX-U theory, modeling and analysis results
Nuclear Fusion·2022· 15 citedOpen Access
page 1 of 6Next →

Frequent Co-authors

Eugenio Schuster(19), Egemen Kolemen(14), David Humphreys(12), J. Ménard(11), M.L. Walker(11), B.G. Penaflor(10), Robert D. Johnson(10), J.R. Ferron(9), S. P. Gerhardt(8), S. Kaye(8), Justin Barton(8), D. Gates(7), T. C. Luce(7), W Wehner(7), J.M. Canik(6), R. Maingi(6), M. Ono(6), Jong-Kyu Park(6), S.A. Sabbagh(6), N.W. Eidietis(6)