人工智能在等离子体领域的应用论文：示例及研究方向

人工智能在等离子体领域的应用论文

以下是一些人工智能应用在等离子体领域的论文示例，涵盖了等离子体形状控制、扰动预测、等离子体湍流分析、等离子体轮廓预测、等离子体湍流控制等多个方向。

1. 'Using deep neural networks for real-time control of plasma shape in tokamaks' by K. F. Chen et al. (2017)
2. 'Machine learning-based prediction of disruptions in tokamak experiments' by T. W. Brooks et al. (2018)
3. 'Application of machine learning techniques for plasma turbulence analysis in fusion devices' by M. G. Shabbir et al. (2019)
4. 'Deep learning for edge-localized mode classification and prediction in tokamaks' by J. Weynants et al. (2020)
5. 'Application of recurrent neural networks for prediction of plasma profiles in tokamaks' by S. C. Mirnov et al. (2021)
6. 'Artificial intelligence-based control of plasma turbulence in magnetically confined fusion plasmas' by F. Felici et al. (2021)
7. 'Deep learning-based plasma disruption prediction using fusion diagnostic data' by Y. Liu et al. (2022)

请注意，这些只是一些示例，还有许多其他相关的论文可供参考。