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PAPERS & POSTERS
Publishing and presenting are an important part of our work, and we encourage our partners and collaborators to do the same.
If you are interested in making a scientific publication with us or using NSFsim or any other solutions for your work or publication, do not hesitate to get in touch.
If you are a journalist, blogger, or investor, do not hesitate to get in touch to discuss the fusion industry, recent events, our plans and progress, or simply to learn more.
NEWS & TALKS
We love fusion and our work! We are always ready to speak about our vision and experience, present our projects and solutions, and help journalists and investors learn more about us and navigate the industry.
TECH BLOG
At Next Step Fusion, we believe in sharing our ideas and results, but scientific peer-reviewed publications take a long time and can appear to be overkill for sharing progress or promoting our tools. That’s why we started blogging.

You can find our tech blog on Medium or subscribe to our LinkedIn newsletter.

Papers

  • 2026

  • Plasma Confinement State Classification in Fusion Power Plants: Profile Reflectometer and Ensemble Diagnostics
    Clark, R., et al.
    Abstract

  • 2025

  • Plasma Confinement State Classification via FPP Relevant Microwave Diagnostics
    Clark, R., et al.
    Abstract
  • Robustness by Design: Interface Contracts for AI Control in High-Stakes Physical Systems
    Glukhov, V., et al.
    Abstract
  • Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning
    Subbotin, G., et al.
    Abstract
  • Summary report from the mini-conference [APS DPP 2024] on Digital Twins for Fusion Research
    Schissel, D. P., et al.
    Abstract
  • Reconstructing the Plasma Boundary with a Reduced Set of Diagnostics
    Stokolesov, M., et al.
    Abstract
  • Validation of NSFsim as a Grad-Shafranov Equilibrium Solver at DIII-D
    Clark, R., et al.
    Abstract
  • Electromagnetic System Conceptual Design for a Negative Triangularity Tokamak
    Guizzo, S., et al.
    Abstract
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Posters

  • 2025

  • [APS DPP 2025] Machine learning approaches to plasma state mode classification via reactor relevant diagnostics at DIII-D
    Clark, R., et al.
    Abstract
  • [APS DPP 2025] Integrated tokamak control using NSFsim simulator
    Subbotin, G., et al.
    Abstract
  • [FEC 2025] Fusion Twin Platform: an innovative tool for fusion research and education
    Zhurba, A., et al.
    Abstract
  • [FEC 2025] Reconstructing the plasma boundary with a reduced set of diagnostics
    Stokolesov, M., et al.
    Abstract
  • [EPS 2025] Fusion Twin Platform: an tnnovative tool for fusion research and education
    Zhurba, A., et al.
    Abstract
  • [EPS 2025] Design and implementation of a reinforcement learning-based plasma shape controller at DIII-D
    Subbotin, G., et al.
    Abstract
  • [EPS 2025] NSFsim code for machine design and scenario development
    Nurgaliev, M., et al.
    Abstract
  • [OSSFE 2025] Developing a state-oriented plasma control system
    Kachkin, A., et al.
    Abstract

  • 2024

  • [APS DPP 2024] NSFsim validation as a DIII-D plasma equilibrium simulator
    Clark, R., et al.
    Abstract
  • [EPS 2024] Validation and verification of synthetic magnetic diagnostics based on free boundary equilibrium solver for DIII-D plasma control system
    Nurgaliev, M., et al.
    Abstract
  • [EPS 2024] Electromagnetic system conceptual design for a negative triangularity tokamak
    Subbotin, G., et al.
    Abstract
  • [EEML 2024] Magnetic feedback control of DIII-D tokamak via deep reinforcement learning
    Granovskiy, A., et al.
    Abstract

Talks

  • 2025

  • [IMEG 17] Integrated modeling for equilibrium, scenarios, and disruption processes in tokamaks with DINA and NSFsim
    Khairutdinov, E., et al.
    Download
  • [PhDiaFusion 2025] Novel tools for tokamak design and control
    Subbotin, G.
    Download
  • Plasma control online meetup - May 6th, 2025
    Zhurba, A., Subbotin, G., Mele, A., Dubbioso, S., Glukhov, V.
    Presentations
  • [AI4X 2025] Magnetic control of tokamak plasma through deep reinforcement learning with privileged information
    Sorokin, D., et al.
    Download

Tech blog

  • Our tech blog is a valuable source of information about our work and our thoughts on the fusion industry. Note that we also cross-post it as a LinkedIn newsletter.
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    LinkedIn newsletter