14.2 Artificial Intelligence and Machine Learning in Fusion Research

Machine learning is revolutionizing fusion research. From optimizing plasma control to enhancing diagnostics, AI algorithms are improving reactor performance and efficiency. These advancements are bringing us closer to achieving sustainable fusion energy.

AI-driven simulations are accelerating reactor design, while digital twins aid in optimization. However, the integration of AI in fusion technology raises ethical concerns. Addressing transparency, bias, and safety issues is crucial for responsible development.

Machine Learning Applications in Fusion Research

Machine learning for plasma optimization

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• Machine learning algorithms optimize plasma control in fusion devices
  • Reinforcement learning finds optimal control strategies through trial and error
    • Agents learn to maximize a reward function based on desired plasma parameters or fusion performance metrics
  • Supervised learning trains models on historical data to predict optimal control settings based on plasma state inputs
    • Trained models enable real-time control decisions
• Optimized control strategies through machine learning improve fusion device performance
  • Better plasma stability and confinement are achieved
  • Increased fusion power output and efficiency (Q factor) can be attained
  • Disruptions and detrimental events (vertical displacement events) are minimized or avoided

Deep learning in fusion diagnostics

• Deep learning techniques enable real-time plasma diagnostics
  • Convolutional Neural Networks (CNNs) analyze plasma images and videos
    • CNNs extract relevant features and patterns from visual data (plasma shape, filaments)
    • Real-time monitoring of plasma position and instabilities is achieved
  • Recurrent Neural Networks (RNNs) process time-series data from plasma sensors (magnetic probes, Langmuir probes)
    • RNNs capture temporal dependencies and dynamics in plasma behavior
    • Predictions of future plasma states and evolution are made
• Anomaly detection in fusion experiments is performed using deep learning
  • Autoencoders trained on normal operating data identify potential anomalies as deviations from reconstructed data
  • Generative Adversarial Networks (GANs) learn to generate realistic plasma data
    • Discriminator network identifies anomalies as deviations from generated data
  • Early detection of instabilities (tearing modes), disruptions, and equipment failures (magnet quenches) is achieved

AI-Driven Simulations and Ethical Considerations

AI simulations for reactor design

• AI-driven simulations accelerate fusion reactor design
  • Machine learning surrogate models replace computationally expensive physics simulations
    • Surrogate models approximate simulation outputs based on input parameters
    • Rapid exploration of design space and optimization of reactor components (divertor, first wall) become possible
  • Generative design algorithms propose novel reactor geometries and configurations
    • Algorithms optimize for specific performance metrics (tritium breeding ratio) and constraints (neutron fluence)
    • Human designers focus on evaluating and refining generated designs
• Digital twins of fusion reactors aid in optimization and operation
  • Virtual replicas of physical reactors are created using AI and simulation
    • Real-time data from sensors is fed into the digital twin
    • Predictive maintenance and performance optimization are achieved
  • Reinforcement learning applied to digital twins finds optimal operation strategies
    • Agents learn to control virtual reactors under various scenarios (plasma current ramp-up)
    • Learned strategies are transferred to physical reactors for improved performance

Ethical challenges of AI in fusion

• Transparency and interpretability of AI models are important ethical considerations
  • Black-box models make decisions without clear explanations, hindering trust and accountability
  • Efforts should focus on developing interpretable AI models
    • Feature importance and decision-making processes should be understandable
    • Human operators should be able to validate and override AI decisions when necessary
• Bias and fairness in AI algorithms must be addressed
  • Training data and algorithms can inadvertently introduce biases, leading to unfair or discriminatory outcomes
  • Rigorous testing and auditing of AI systems are necessary
    • Diverse datasets and fairness metrics should be used during development
    • Ongoing monitoring and correction of biases should be implemented
• Robustness and safety of AI-controlled fusion reactors are critical challenges
  • AI systems must be resilient to uncertainties, disturbances, and adversarial attacks
    • Robust control strategies and fail-safe mechanisms should be incorporated
  • Thorough validation and verification of AI components are essential
    • Simulation-based testing and hardware-in-the-loop validation should be performed
    • Gradual deployment and human oversight should be maintained