AI-Driven Advancements in Nuclear Fusion Energy
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B4: Nuclear Energy".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 414
Special Issue Editors
Interests: fusion engineering; artificial intelligence; signal, image and data processing; nuclear fusion; plasma monitoring and control; nuclear energy
Special Issue Information
Dear Colleagues,
The pursuit of commercial nuclear fusion as a sustainable, nearly limitless energy source is advancing rapidly. Fusion promises to provide safe, clean, and nearly infinite energy. For this reason, fusion research gained significant interest from both private companies and government programs. Yet, achieving commercial fusion remains a complex challenge and requires substantial investment, a skilled workforce, and international cooperation to share expertise and experimental data.
Nuclear fusion experiments are a perfect testbed for data-driven analysis due to the large amount of data available and the need to bridge the gap between theory and practical application. For this reason, artificial intelligence (AI) and machine learning (ML) have become essential tools in fusion research. AI has proven its value across many industries by solving complex, data-intensive challenges, and its impact in fusion is similarly transformative.
We welcome original research, reviews, and case studies in the following areas:
- AI for Data Validation and Diagnostics: Accurate diagnostics are fundamental to understanding fusion plasma behaviour. We seek contributions on innovative processing methods for data and image validation, error estimation, and image reconstruction, enhancing the accuracy of plasma diagnostics and measurement reliability.
- Predictive Modelling and Real-Time Control: Predictive capabilities of AI are essential in modelling plasma behaviour, predicting disruptions, and automatically detecting events. Moreover, surrogate AI models may improve real-time control capabilities in fusion experiments. Research on the use of AI for event detection, plasma control, and machine protection is encouraged.
- Simulation and Design Optimization: Data-driven optimisation methods have the capability to speed up future reactor design. On one hand, it can provide more efficient models to evaluate design choices, and on the other hand, it can automate the manual parameter calibration of complex codes.
- Emerging AI Applications in Fusion: Authors are also encouraged to submit work on novel AI techniques, such as neural networks, dimensionality reduction, and reinforcement learning, tailored to fusion research, with a focus on complex data processing, data visualisation, model identification, and real-time analysis.
This Special Issue aims to showcase the latest AI-driven advancements in nuclear fusion, highlighting interdisciplinary approaches that pave the way for future collaboration. By focusing on how AI addresses the critical barriers in fusion research, this collection will serve as a foundational resource for researchers working towards a future of safe, sustainable fusion energy.
Dr. Fabio Pisano
Dr. Enrico Aymerich
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- data-driven methods
- tokamak
- stellarator
- real-time control
- predictive modelling
- data validation
- diagnostics
- simulation
- design optimization
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