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

TL;DR

This paper introduces an AI-based control system using backpropagation neural networks to manage the complex and extreme conditions of proton-boron fusion reactors, aiming for stable, efficient, and scalable fusion energy.

Contribution

It presents a novel neural network approach for autonomous control of p-11B fusion reactors, addressing challenges of plasma variability with real-time learning.

Findings

Effective real-time control of plasma parameters demonstrated

Enhanced stability and efficiency in fusion reactor operation

Potential scalability to other fusion systems

Abstract

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.