About

Egemen Kolemen is a Professor at Princeton University's Mechanical & Aerospace Engineering department, with joint appointments at the Andlinger Center for Energy and the Environment and the Princeton Plasma Physics Laboratory (PPPL). He serves as the director of the Program in Sustainable Energy and has been recognized with the David J. Rose Excellence in Fusion Engineering Award and the American Nuclear Society's Technical Accomplishment Award. Additionally, he is an ITER Scientist Fellow. His research integrates engineering and physics analysis to advance the development of economically feasible fusion reactors. Professor Kolemen leads cutting-edge research on machine learning, real-time diagnostics, and control at major fusion facilities including KSTAR, NSTX-U, and DIII-D. He also directs laboratories focused on liquid metal divertors and low temperature diagnostics. On the theoretical front, his group develops software tools for stellarator optimization and economic analysis of fusion reactors, contributing to the broader goal of enabling practical fusion energy.

Research topics

• Physics
• Artificial Intelligence
• Nuclear physics
• Computer Science
• Engineering
• Nuclear engineering
• Machine Learning
• Computational physics
• Materials science
• Mechanical engineering
• Metallurgy
• Programming language
• Telecommunications
• Composite material
• Mechanics

Selected publications

• Publisher's Note: “Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability” [Phys. Plasmas <b>33</b> , 032502 (2026)]

  Physics of Plasmas · 2026-04-01

  articleSenior author
  PublisherDOI

• Regulation compliant AI for fusion: explainable image-based feedback control of divertor detachment in DIII-D tokamak

  Nuclear Fusion · 2026-01-16 · 1 citations

  preprintOpen accessSenior author

  Abstract While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI-enabled linear and interpretable control system for successful divertor detachment control with the DIII-D lower divertor camera. Using D 2 gas, we demonstrate successful feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment. This automatic training and linear processing framework can be extended to any image-based diagnostic for future fusion reactors.

  PublisherDOI

• Real-time plasma monitoring framework for advanced plasma control and ML-research in DIII-D

  Fusion Engineering and Design · 2026-04-16

  articleSenior author
  PublisherDOI

• Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability

  Physics of Plasmas · 2026-03-01 · 1 citations

  articleOpen accessSenior author

  Artificial intelligence models have demonstrated strong predictive capabilities for various instabilities in fusion devices such as Tokamaks, including tearing modes (TM), edge localized modes, and disruptive events, but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, core electron temperature and rotation peaking play the primary role in TM stability, while density changes have smaller effects on stability. We show that off-axis ion temperature stabilizes TMs, suggesting that off-axis neutral beam heating can further stabilize this scenario. This work presents a generalizable ML-based event prediction methodology, from training to physics-driven interpretation, bridging the gap between physics understanding and opaque ML models.

  PublisherDOI

• Unified ELM suppression on KSTAR and DIII-D via adaptive feedback control strategies

  Nuclear Fusion · 2025-07-10 · 2 citations

  articleOpen accessSenior authorCorresponding

  Abstract This paper reports on the extension of our amplitude-based resonant magnetic perturbation (RMP) edge localized mode (ELM) controller to support phasing control (relative toroidal phases of RMP waveforms between rows of coils), multiple toroidal mode numbers, and new ‘jump’ and ‘probing’ strategies, all deployed on KSTAR and DIII-D. By treating the control algorithm as device-independent and adjusting only the real-time interfaces to sensors and power supplies, we have confirmed that the same finite state machine—based feedback logic can be ported between machines with minor modifications. In experiments using n = 2 RMPs on KSTAR and n = 3 on DIII-D, the controller successfully modulated RMP amplitudes in real time to sustain ELM suppression while minimizing confinement degradation. Phasing control broadened the suppression window, as it permitted the system to avoid locked-mode regions and safely access ELM-free conditions. A rotating RMP phasing scheme, integrated into the same framework, distributes divertor heat loads more uniformly, making it a promising strategy for protecting plasma-facing components during long discharges. New ‘jump’ and ‘probing’ techniques demonstrate the possibility for the controller to preempt imminent ELMs and refine the minimum required RMP amplitude without returning to ELMy conditions. Taken together, these upgrades enable extended ELM-free operation while mitigating confinement degradation, providing a practical framework for real-time ELM control in future high-performance tokamaks.

  PublisherDOI

• Assessing the numerical stability of physics models to equilibrium variation through database comparisons on DIII-D

  Plasma Physics and Controlled Fusion · 2025-10-22 · 3 citations

  articleOpen access

  Abstract High fidelity kinetic equilibria are crucial for tokamak modeling and analysis. Manual workflows for constructing kinetic equilibria are time consuming and subject to user error, motivating development of automated equilibrium reconstruction tools to provide accurate and consistent reconstructions for downstream physics analysis. These automated tools also provide access to kinetic equilibria at large database scales, which enables the quantification of general uncertainties arising from equilibrium reconstruction techniques. In this paper, we compare a large database of DIII-D kinetic equilibria generated manually by physics experts to equilibria from automated kinetic reconstruction tools, assessing the impact of reconstruction method on equilibrium parameters and resulting magnetohydrodynamic stability calculations. We find agreement among scalar parameters, whereas profile quantities, such as the bootstrap current, show larger disagreements. We analyze ideal kink and classical tearing stability with DCON and STRIDE respectively, finding that the kink stability calculation is generally more robust than the tearing index <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi mathvariant="normal">Δ</mml:mi> <mml:mi>′</mml:mi> </mml:msup> </mml:mrow> </mml:math> calculation. We find that in 90% of cases, both kink stability classifications are unchanged between the manual expert and automated kinetic equilibria.

  PublisherDOI

• TorbeamNN: Machine learning based steering of ECH mirrors on KSTAR

  arXiv (Cornell University) · 2025-04-15

  preprintOpen accessSenior author

  We have developed TorbeamNN: a machine learning surrogate model for the TORBEAM ray tracing code to predict electron cyclotron heating and current drive locations in tokamak plasmas. TorbeamNN provides more than a 100 times speed-up compared to the highly optimized and simplified real-time implementation of TORBEAM without any reduction in accuracy compared to the offline, full fidelity TORBEAM code. The model was trained using KSTAR electron cyclotron heating (ECH) mirror geometries and works for both O-mode and X-mode absorption. The TorbeamNN predictions have been validated both offline and real-time in experiment. TorbeamNN has been utilized to track an ECH absorption vertical position target in dynamic KSTAR plasmas as well as under varying toroidal mirror angles and with a minimal average tracking error of 0.5cm.

  PublisherDOI

• Extending near-axis equilibria in DESC

  ArXiv.org · 2025-06-05

  preprintOpen accessSenior author

  The near-axis description of optimised stellarator fields has proven to be a powerful tool both for design and understanding of this magnetic confinement concept. The description consists of an asymptotic model of the equilibrium in the distance from its centermost axis, and is thus only approximate. Any practical application therefore requires the eventual construction of a global equilibrium. This paper presents a novel way of constructing global equilibria using the \texttt{DESC} code that guarantees the correct asymptotic behaviour imposed by a given near-axis construction. The theoretical underpinnings of this construction are carefully presented, and benchmarking examples provided. This opens the door to an efficient coupling of the near-axis framework and that of global equilibria for future optimisation efforts.

  PublisherOA PDFDOI

• Control of pedestal-top electron density using RMP and gas puff at KSTAR

  ArXiv.org · 2025-06-25

  preprintOpen accessSenior author

  We report the experimental results of controlling the pedestal-top electron density by applying resonant magnetic perturbation with the in-vessel control coils and the main gas puff in the 2024-2025 KSTAR experimental campaign. The density is reconstructed using a parametrized psi_N grid and the five channels of the line-averaged density measured by a two-colored interferometer. The reconstruction procedure is accelerated by deploying a multi-layer perceptron to run in about 120 microseconds and is fast enough for real-time control. A proportional-integration controller is adopted, with the controller gains being estimated from the system identification processes. The experimental results show that the developed controller can follow a dynamic target while exclusively using both actuators. The absolute percentage errors between the electron density at psi_N=0.89 and the target are approximately 1.5% median and a 2.5% average value. The developed controller can even lower the density by using the pump-out mechanism under RMP, and it can follow a more dynamic target than a single actuator controller. The developed controller will enable experimental scenario exploration within a shot by dynamically setting the density target or maintaining a constant electron density within a discharge.

  PublisherOA PDFDOI

• Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

  Nature Communications · 2025-09-26 · 4 citations

  articleOpen accessSenior author

  Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery. Sensor failures and limited resolution challenge many complex systems. Here, authors develop a multimodal AI method to generate super-resolution of a sensor using other available sensors in the system, revealing hidden dynamics in fusion plasmas and enabling cost-effective, high-resolution diagnostics.

  PublisherOA PDFDOI

Frequent coauthors

• A. Nelson

  91 shared

• A. Diallo

  Princeton Plasma Physics Laboratory

  85 shared

• D. R. Hatch

  67 shared

• R. J. Groebner

  General Atomics (United States)

  67 shared

• Michael Halfmoon

  The University of Texas at Austin

  66 shared

• S. M. Mahajan

  Fusion (United States)

  65 shared

• M. Kotschenreuther

  64 shared

• M. Curie

  Princeton University

  57 shared

Labs

• Plasma Control GroupPI

  Research on plasma physics and fusion

Awards & honors

• David J. Rose Excellence in Fusion Engineering Award
• American Nuclear Society’s Technical Accomplishment Award
• ITER Scientist Fellow