实验技术与管理2025，Vol.42Issue(4)：1-13,13.DOI:10.16791/j.cnki.sjg.2025.04.001
大数据与AI技术在磁约束聚变领域的应用与展望
Application and prospects of big data and AI technology in magnetic confinement fusion
摘要
Abstract
[Significance]The rapid development of big data and artificial intelligence(AI)technologies has greatly advanced research on magnetic confinement fusion(MCF),which is a promising approach to achieving sustainable fusion energy.Because of the complexity of plasma dynamics,including nonlinear and high-dimensional processes,conventional methods for simulation,monitoring,and control have faced significant limitations in accuracy and computational efficiency.By integrating AI and big data,researchers can now address many of these challenges,leading to significant improvements in the precision and speed of simulations as well as real-time analysis of experimental data.The application of these technologies has become essential for advancing fusion research,particularly in facilitating the control and optimization of plasma confinement,a key factor in achieving sustained nuclear fusion reactions.[Progress]The application of AI in MCF has led to numerous advances across different areas.In plasma simulation,AI-based methods,such as machine learning-driven surrogate models,have significantly reduced computational costs while maintaining or even enhancing accuracy.These models enable the rapid prediction of plasma behavior in response to varying conditions,which is crucial for optimizing experimental parameters and ensuring the stability of plasma confinement.The capability of AI to manage large-scale,high-dimensional data has proven particularly beneficial for multiscale simulations that involve complex interactions between physical processes.In experimental monitoring and control,AI,combined with big data analytics,has enabled real-time processing of sensor data from fusion devices.Through predictive modeling and adaptive control mechanisms,AI algorithms can detect potential anomalies and make autonomous adjustments to operational parameters,thereby improving the reliability and safety of fusion experiments.The dynamic nature of plasma requires precise and immediate responses to fluctuations,and the capability of AI to analyze past experimental data and predict future behavior has enabled more effective management of plasma instabilities,thereby enhancing the overall system robustness and contributing to the optimization of plasma performance.AI has also been instrumental in the design and optimization of fusion devices.By employing AI to model magnetic field configurations and predict material performance under extreme conditions,researchers have been able to improve the durability and efficiency of critical reactor components.These advancements include optimizing the design of superconducting magnets and plasma-facing materials,both of which are essential for the long-term operation of fusion reactors.AI-driven optimization has resulted in improved magnetic confinement configurations,ensuring better plasma stability and enhanced confinement performance,which are necessary for achieving continuous fusion reactions.Furthermore,AI facilitates interdisciplinary collaboration by integrating data from diverse fields,such as plasma physics,materials science,and computational modeling.The use of AI in cross-disciplinary research fosters innovation and accelerates progress in addressing key challenges in fusion research.Moreover,AI has contributed to the development of intelligent educational platforms and virtual experimentation environments,enabling researchers and students to gain hands-on experience through simulations and virtual experiments.These platforms are crucial for advancing knowledge and skills in plasma physics and fusion technology and help cultivate the next generation of fusion researchers.[Conclusions and Prospects]The future of MCF research will be increasingly shaped by the integration of AI and big data technologies.The capability of AI to enhance simulation accuracy,optimize experimental design,and improve real-time control systems will play a central role in overcoming existing technical barriers in fusion research.Furthermore,AI-driven materials research will contribute to the discovery and design of new materials capable of withstanding the harsh conditions inside fusion reactors,thus ensuring longer operational lifespans and increased reactor efficiency.As AI technologies continue to evolve,they are expected to play a more significant role in all levels of fusion research,from experimental planning to real-time plasma control and material optimization.These advancements will not only accelerate progress toward realizing practical fusion energy but also contribute to the development of novel technologies that support the broader scientific community.In addition,AI-powered educational platforms will continue to provide researchers with advanced tools for learning and experimentation,helping them bridge the gap between theory and practical application.The continued development of AI and big data in this field holds great promise for the successful realization of MCF as a viable energy source for the future.关键词
磁约束聚变/大数据/人工智能/模拟计算/实验监控Key words
magnetic confinement fusion/big data/artificial intelligence/simulation computation/experimental monitoring分类
数理科学引用本文复制引用
张小西,段思哲,高宝峰,迟浩,桂南..大数据与AI技术在磁约束聚变领域的应用与展望[J].实验技术与管理,2025,42(4):1-13,13.基金项目
国家自然科学基金青年科学基金(12405261,12405263) （12405261,12405263）
三束材料改性教育部重点实验室开放基金(KF2502) （KF2502）
齐鲁工业大学(山东省科学院)人才科研项目(2023RCKY140) （山东省科学院）
