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Abstract <p>Currently, large-scale studies in the field of controlled thermonuclear fusion (CTF) are being conducted in many countries. This is due to the potential possibility of obtaining a virtually inexhaustible source of energy. At the same time, the theoretical basis of the research is mathematical modeling for describing the state of the plasma and numerical methods for solving the equations obtained. However, in recent years, machine learning algorithms have become increasingly popular in problems of CTF. This paper is devoted to a review of new neural network algorithms for plasma control in a tokamak and predictive neural network algorithms in applications to problems of CTF.</p>

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Machine Learning Applied to Problems of Nuclear Fusion

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • E. I. Chetkin,
  • A. G. Shishkin

摘要

Abstract

Currently, large-scale studies in the field of controlled thermonuclear fusion (CTF) are being conducted in many countries. This is due to the potential possibility of obtaining a virtually inexhaustible source of energy. At the same time, the theoretical basis of the research is mathematical modeling for describing the state of the plasma and numerical methods for solving the equations obtained. However, in recent years, machine learning algorithms have become increasingly popular in problems of CTF. This paper is devoted to a review of new neural network algorithms for plasma control in a tokamak and predictive neural network algorithms in applications to problems of CTF.