用神经网络自动调控质子-硼聚变反应堆，实现稳定高效运行。
AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks
- 基于反向传播神经网络实时学习等离子体数据，自主调节关键参数。
- 可适应极端高温与高能粒子的非线性动态变化，提升控制稳定性。
- 方法具备扩展性，适用于其他聚变系统及未来智能控制技术。
质子-硼（p-11B）聚变有望实现可持续、无中子的能源生产。然而，其实施面临极端运行条件的挑战，如等离子体温度超过百亿摄氏度，以及高能粒子控制复杂。传统控制系统难以应对等离子体高度动态和非线性的行为。本文提出一种新方法，利用基于反向传播的神经网络，实现质子-硼聚变反应堆关键参数的自主控制。该方法通过实时反馈与物理数据学习，自适应变化的等离子体状态，为实现稳定高效的p-11B聚变提供潜在突破。此外，我们拓展了该方法在其他聚变系统及未来人工智能技术中的可扩展性与泛化能力。
原文摘要 · Abstract (English)
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.
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