AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909352733966336 |
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| author | Laurelli, Michele |
| author_facet | Laurelli, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks Laurelli, Michele Plasma Physics Artificial Intelligence Machine Learning Systems and Control 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. |
| title | AI-Driven Autonomous Control of Proton-Boron Fusion Reactors Using Backpropagation Neural Networks |
| topic | Plasma Physics Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2410.12871 |