AI applications in plasma physics may open up possibilities for fusion energy

Machine learning’s emerging role in plasma physics: Machine learning (ML) techniques are increasingly being applied to computational plasma physics, offering new opportunities for enhancing scientific understanding and improving numerical modeling of complex plasma systems.

• ML tools enable the transformation of simulation and experimental data into useful and explainable science, augmenting domain knowledge in plasma physics.
• ML-enhanced numerical modeling has the potential to revolutionize scientific computing for complex engineering systems, allowing for detailed examination and automated optimization of plasma technologies.

Current state of ML applications: While machine learning has seen significant growth in various scientific domains, particularly in fluid mechanics, its application in numerical plasma physics research remains relatively limited.

• The close relationship between fluid mechanics and plasma physics presents an opportunity to create a roadmap for transferring ML advances in fluid flow modeling to computational plasma physics.
• This perspective aims to outline such a roadmap, discussing fundamental aspects of ML and its potential applications in plasma physics.

Fundamental aspects of ML: A new paper discusses general aspects of machine learning, including various categories of ML algorithms and the types of problems that can be addressed using ML techniques.

• Different categories of ML algorithms are explored, such as supervised learning, unsupervised learning, and reinforcement learning.
• The authors examine various problem types that ML can help solve, including classification, regression, and dimensionality reduction.

ML applications in computational fluid dynamics: The same paper reviews several insightful prior efforts in the use of ML for computational fluid dynamics (CFD), providing specific examples for each problem type.

• These examples serve as a basis for understanding how similar techniques could be applied to computational plasma physics.
• The authors highlight successful applications of ML in areas such as turbulence modeling, flow control, and reduced-order modeling in CFD.

Recent ML applications in plasma physics: The research also reviews recent ML applications in plasma physics for each problem type identified earlier.

• Examples include the use of ML for plasma diagnostics, fusion plasma control, and plasma simulation acceleration.
• The authors discuss how these applications demonstrate the potential of ML in advancing computational plasma physics.

Future directions and development pathways: The paper outlines promising future directions and development pathways for ML in plasma modeling within different application areas.

• Potential areas for growth include ML-enhanced reduced-order modeling, surrogate modeling for plasma simulations, and ML-driven plasma control strategies.
• The authors emphasize the importance of developing ML techniques that can handle the unique challenges posed by plasma systems, such as multi-scale physics and complex geometries.

Challenges and opportunities: Moreover, the paper identifies prominent challenges that must be addressed to realize ML’s full potential in computational plasma physics.

• One key challenge is the need for cost-effective high-fidelity simulation tools for extensive data generation, which is crucial for training ML models.
• The authors also discuss the importance of developing interpretable ML models that can provide physical insights into plasma phenomena.
• Opportunities for addressing these challenges include collaborative efforts between plasma physicists and ML experts, as well as the development of specialized ML architectures for plasma physics applications.

Broader implications: The integration of machine learning techniques into computational plasma physics has the potential to significantly advance our understanding of plasma systems and improve the design and operation of plasma-based technologies.

• Successful implementation of ML in plasma physics could lead to breakthroughs in areas such as fusion energy research, plasma-based propulsion systems, and plasma processing technologies.
• The roadmap presented in this perspective provides a foundation for future research efforts aimed at leveraging ML techniques to address complex challenges in plasma physics and engineering.