# AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks

arXiv: https://arxiv.org/abs/2410.12871 · HTML: https://mlsanity.com/paper/2410.12871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)

## Abstract (arXiv)

Proton-boron (p-11B) fusion presents a promising path towards sustainable, neutron-free energy generation. However, its implementation is hindered by extreme operational conditions, such as plasma temperatures exceeding billions of degrees and the complexity of controlling high-energy particles. Traditional control systems face significant challenges in managing the highly dynamic and non-linear behavior of the plasma. In this paper, we propose a novel approach utilizing backpropagation-based neural networks to autonomously control key parameters in a proton-boron fusion reactor. Our method leverages real-time feedback and learning from physical data to adapt to changing plasma conditions, offering a potential breakthrough in stable and efficient p-11B fusion. Furthermore, we expand on the scalability and generalization of our approach to other fusion systems and future AI technologies.