Programme Parameters
Advanced Postgrad
5 Days (7.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This intensive 5-day course equips researchers with cutting-edge artificial intelligence techniques to accelerate nuclear fusion from laboratory experiments to grid-ready power plants in the 2030s. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive 5-day course equips researchers with cutting-edge artificial intelligence techniques to accelerate nuclear fusion from laboratory experiments to grid-ready power plants in the 2030s.
📋 Course Objectives
1. Apply AI Enablement methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A demonstrable AI Enablement project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Confinement Physics Essentials
• Magnetic confinement configurations: tokamak and stellarator geometry
• MHD equilibrium, transport and the instabilities that limit performance
• Diagnostics: what is actually measured inside a burning plasma, and how sparsely
Computational Models and Their Cost
• Gyrokinetic and MHD codes and where their expense becomes prohibitive
• Surrogate modelling to replace inner loops of expensive simulation
• Validation against experiment rather than against other simulations
Reinforcement Learning for Plasma Control
• Shape and position control as a control problem with hard safety limits
• Reinforcement learning trained in simulation and the sim-to-real gap
• Disruption prediction and mitigation within actuator response times
Optimisation of Reactor Configurations
• Stellarator coil optimisation and high-dimensional design spaces
• Multi-objective trade-offs across confinement, engineering and cost
• Uncertainty propagation from physics models into design decisions
From Physics to Plant
• Tritium breeding, materials survivability and the neutron problem
• Reading a fusion company's technical claims critically
• Where machine learning genuinely shortens the path and where it does not
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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