Algorithmic Plasma Physics: AI-Accelerated Nuclear Fusion Commercialization

Aim

Workshop Objectives

Workshop Structure

Day 1: The AI-Fusion Digital Convergence and the 2030s Commercialization Roadmap This foundational day sets the strategic vision for fusion power in the AI era, showing how algorithmic intelligence is compressing decades of development into the mid-2030s grid-ready timeline.

Topics Covered:

• US Department of Energy’s “Build-Innovate-Grow” strategy and global fusion deployment targets
• Architecting an AI-Fusion Digital Convergence Platform to close critical science & technology gaps
• Global investment landscape, private-sector momentum, and AI-powered electricity-mix forecasting scenarios

Hands-on Focus (Free Tools):

• Data exploration and visualization of public fusion & energy datasets using Jupyter/Colab, pandas, NumPy, and matplotlib/Plotly/Seaborn
• Building simple predictive models for energy demand and investment trends with scikit-learn
• Group exercise: Conceptual design of an open-source AI-Fusion platform

Day 2: Deep Reinforcement Learning for Magnetic Confinement and Plasma Sculpting Participants dive into autonomous control of nonlinear plasma dynamics inside tokamaks — the core challenge for stable, wall-contact-free operation.

Topics Covered:

• Physics of plasma stability, magnetic-coil control, and heat-exhaust management
• Landmark case studies (Google DeepMind + EPFL) on deep reinforcement learning for real-time plasma shaping
• Fast differentiable simulators (TORAX and simplified open-source variants) for rapid scenario testing

Hands-on Focus (Free Tools):

• Creating a custom OpenAI-Gym-style plasma-control environment
• Training deep RL agents using Stable Baselines3 or Ray RLlib (PyTorch/TensorFlow backend)
• Live optimization of control policies and performance analysis

Day 3: Disruption Prediction and Interpretable AI in Reactor Safety Safety and trustworthiness are non-negotiable for commercial fusion. This day focuses on real-time disruption forecasting and explainable AI to bridge black-box models with physical understanding.

Topics Covered:

• Machine-learning approaches (CNNs, random forests, physics-informed models) for tearing-mode and instability prediction
• Real-time plasma control using sensor data from major tokamaks
• Explainable AI techniques to interpret model decisions and ensure regulatory-grade reliability

Hands-on Focus (Free Tools):

• Building and evaluating disruption-prediction models on mock fusion sensor datasets (CNNs for time-series, Random Forests for tabular data)
• Applying SHAP (Shapley Additive Explanations) library for feature importance and model interpretability
• Comparative analysis of black-box vs. physics-informed predictions

Day 4: Immersive Digital Twins and Steady-State Operation Management Learn how AI-powered digital twins enable virtual testing of reactor performance before any physical build, solving extreme heat-flux and steady-state challenges in projects like ITER.

Topics Covered:

• Multi-fidelity modeling: combining high-fidelity physics simulators with fast data-driven surrogates
• AI-driven steady-state operation strategies for long-pulse tokamaks
• Digital-twin architectures for predictive maintenance and performance optimization

Hands-on Focus (Free Tools):

• Designing data-ingestion pipelines using open-source tools (Apache Airflow local or Prefect)
• Building interactive digital-twin dashboards with Plotly Dash or Streamlit
• Running simplified TORAX + neural-operator surrogates for heat-flux prediction

Day 5: Generative AI for Extreme Materials and Fuel-Cycle Optimization + Workshop Synthesis The final day shifts to materials engineering and fuel-cycle challenges, closing with a forward-looking roadmap for public-private partnerships.

Topics Covered:

• Generative AI and graph neural networks for discovering neutron-resistant structural materials
• Machine-learning optimization of the tritium breeding cycle and fuel handling
• Workshop synthesis: next steps for computational infrastructure, open-source community building, and collaborative research

Hands-on Focus (Free Tools):

• Implementing simplified generative models (Variational Autoencoders or Graph Neural Networks) for molecular/material design in PyTorch
• Prompt engineering and ideation with open-weight LLMs (Llama 3.1 or Mistral via Hugging Face / Ollama)
• Group capstone: Drafting a mini research proposal or open-source contribution plan

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure

What You’ll Gain