Minisymposium Presentation

Recent advances in Artificial Intelligence (AI), Machine Learning (ML), and data-driven methods have opened promising pathways for addressing complex computational and control challenges in nuclear fusion and Tokamak research. Tokamak devices, a key configuration for magnetic confinement fusion, produce extensive and complex datasets that present unique challenges due to their multi-physics, nonlinear dynamics, and high-dimensional parameter spaces. This abstract introduces key computational challenges in fusion research, highlighting the critical importance of large-scale data analysis and advanced AI techniques for interpreting experimental results, optimizing plasma control strategies, and reducing operational uncertainties in contexts where high-bandwidth diagnostics produce large amounts of data, even during real-time operations. We will discuss how AI-driven surrogate and reduced-order models can effectively complement or even replace computationally expensive, high-fidelity simulation codes in certain scenarios, significantly accelerating analysis and enabling physics discovery. Another important aspect is the integration of real-time ML algorithms, transforming plasma control strategies and leading to significant performance improvements in extremely challenging fields such as plasma stability and disruption prediction. Specific examples and results from recent experimental and computational studies will be presented, demonstrating the efficacy and reliability of these data-driven approaches.