AI Assisted Reactor Design

AI Powered Gyrokinetic Stability Analysis

Using a fine-tuned LLM we are training our first model Laurelin 1.0 to be able to model the stability constraints on reactors.

Finding an idea Plasma 'Shape'

Finding the ideal plasma shape is crucial for optimising fusion performance, and our AI plays a key role in this process. By analysing vast amounts of data from plasma behaviour, our AI system can quickly identify the most efficient shapes that balance the pressure and magnetic forces required for stability. This allows us to fine-tune the plasma shape in real-time, ensuring it stays optimally configured for fusion. With AI-driven precision, we can achieve a level of control and accuracy that significantly enhances the likelihood of sustained and efficient fusion reactions.

Designing a reactor

By leveraging advanced machine learning algorithms, our AI can autonomously design every aspect of the reactor, from plasma containment to the choice of materials and optimal reactor configuration. Through continuous analysis of vast datasets, it can simulate countless designs and fine-tune them to achieve the ideal conditions for sustainable fusion. With this capability, we are confident that our AI will be able to independently design a fusion reactor capable of achieving positive Q within just two years, accelerating the path to practical fusion energy.

Stability Analysis

One of the biggest challenges in achieving fusion energy is maintaining plasma stability. In a fusion reactor, when atomic nuclei collide in the plasma, they need to be confined long enough and at the right conditions to fuse. However, if the plasma becomes unstable, these particles can scatter, causing the collisions to fail. This instability prevents the reactor from reaching the necessary conditions for sustained fusion, making it difficult to achieve a net energy gain from the process.

Fine tuning Deep Seek

To achieve this, we are fine-tuning a modified version of Deep Seek. Our version has been customised to handle the complex simulations and design tasks required for achieving a positive Q. Running on a high-performance H200 stack, our AI is equipped with the computational power needed to process vast amounts of data and iteratively refine reactor designs. This powerful combination of Deep Seek’s advanced capabilities and the H200’s processing speed allows us to accelerate the design process, bringing us closer to a functional fusion reactor with positive Q in just two years.