The Digital Twin Approach to Fusion Igniter Performance Based on Artificial Intelligence

Based on the digital twin method of artificial intelligence fusion ignition device performance, two sets of deep neural networks with similar structure are used, and with the help of the numerical simulation performance model of pulsed-power high-current device established in accordance with the principle of pulsed-power device and the process of magnetohydrodynamics and radiohydrodynamics, it effectively solves the problem of the data-driven model relying solely on the data and ignoring the actual physical process, which leads to the low accuracy and the huge amount of required data. Pain points. Based on the artificial intelligence deep learning method, a new performance digital twin model of pulsed power high current device and fusion ignition is constructed, and key technologies such as artificial intelligence and maximum entropy principle acceleration strategy are adopted to greatly improve the training speed and accuracy of the digital twin. In this paper, by using a digital twin model based on the deep learning method of artificial intelligence, we have successfully solved the problems of low accuracy and huge amount of required data in the performance modelling of fusion ignition device. The model has achieved remarkable results in improving the training speed and accuracy, providing important theoretical guidance and technical support for the performance optimisation and design of fusion ignition devices. Future research can further explore more applications of AI technology in the field of fusion and promote the development and application of fusion technology.