标题： 通过人工智能进行激光直接驱动压缩实验的预测流体力学模拟 显示英文标题

标题： Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence

摘要： 这项工作展示了由人工智能（AI）增强的预测水动力学模拟，用于激光驱动的内爆实验，以双锥点火（DCI）方案为例。 建立了一个基于Transformer的深度学习模型MULTI-Net，根据激光波形和靶半径预测内爆特征。 提出了一种物理信息解码器（PID），用于高维采样，与拉丁超立方采样相比，显著降低了预测误差。 应用于SG-II升级设施上的DCI实验，MULTI-Net模型能够预测通过X射线条纹相机测量的内爆动力学。 发现大约65%的有效激光吸收因子适用于DCI-R10实验的一维模拟。 对于第33次射击，平均内爆速度和碰撞等离子体密度分别达到195 km/s和117 g/cc。 这项研究展示了一个数据驱动的AI框架，提高了对复杂激光聚变实验模拟的预测能力。 显示英文摘要

摘要： This work presents predictive hydrodynamic simulations empowered by artificial intelligence (AI) for laser driven implosion experiments, taking the double-cone ignition (DCI) scheme as an example. A Transformer-based deep learning model MULTI-Net is established to predict implosion features according to laser waveforms and target radius. A Physics-Informed Decoder (PID) is proposed for high-dimensional sampling, significantly reducing the prediction errors compared to Latin hypercube sampling. Applied to DCI experiments conducted on the SG-II Upgrade facility, the MULTI-Net model is able to predict the implosion dynamics measured by the x-ray streak camera. It is found that an effective laser absorption factor about 65\% is suitable for the one-dimensional simulations of the DCI-R10 experiments. For shot 33, the mean implosion velocity and collided plasma density reached 195 km/s and 117 g/cc, respectively. This study demonstrates a data-driven AI framework that enhances the prediction ability of simulations for complicated laser fusion experiments.