The Data Science Ecosystem for Enabling Fusion Energy

Realizing fusion energy requires rapid analysis, predictive modeling, and control optimization within a dynamic and complex experimental environment. To address these challenges, we propose a Fusion Data Science Ecosystem designed to unlock transformative capabilities. This ecosystem will feature: 1. High-Fidelity Data Analysis: Prompt, physics-informed analysis of fusion facility data, leveraging distributed supercomputing capability, will enable rapid scientific insights; 2. Data Platform for Machine Learning: A robust data platform will facilitate the development of machine learning models from data and simulations, preparing them for integration into real-time control systems and digital twin environments. 3. Digital Twin for Optimization: A digital twin, mirroring the fusion device, will allow exploration and optimization of plasma discharge trajectories. Optimized scenarios can then inform control room decisions, enabling improved performance of the fusion facility. This integrated ecosystem promises to streamline experimental processes, accelerate scientific understanding, and drive us closer to the goal of sustainable fusion energy production.