用AI预测激光聚变冲击实验，提升仿真精度与效率。
Predictive Hydrodynamic Simulations for Laser Direct-drive Implosion Experiments via Artificial Intelligence
- 基于Transformer的MULTI-Net模型，根据激光波形和靶半径预测冲击特征。
- 实验中平均冲击速度达195 km/s，碰撞等离子体密度为117 g/cc。
- 物理引导解码器降低误差，适合高维参数空间的聚变实验预测。
本文提出一种基于人工智能的预测性流体动力学模拟方法，用于激光驱动冲击实验，以双锥点火（DCI）方案为例。构建了基于Transformer的深度学习模型MULTI-Net，根据激光波形和靶半径预测冲击特征。提出物理信息引导解码器（PID），显著降低高维采样误差，优于拉丁超立方采样。该模型应用于SG-II升级装置上的DCI实验，成功预测了X射线条纹相机测得的冲击动力学。研究发现，一维模拟中约65%的有效激光吸收率适用于DCI-R10实验。在第33次实验中，平均冲击速度达到195 km/s，碰撞等离子体密度为117 g/cc。本研究展示了数据驱动的AI框架如何提升复杂激光聚变实验的预测能力。
原文摘要 · Abstract (English)
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.
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