迁移学习在仿真中再现高保真NLTE不透明度的方法
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations
摘要：
高能量密度物理的模拟通常需要非局域热力学平衡（NLTE）不透明度数据。然而，这种数据在相对低的保真度下产生是昂贵的。在高保真度下更是如此，不透明度计算可以贡献总计算时间的95%。这个比例甚至可以达到很大的比例。神经网络可以用来代替低保真度数据的标准计算，并且神经网络可以被训练以再现人工的高保真不透明度光谱。在这项工作中，它表明，一种新的神经网络架构训练，通过迁移学习再现高保真氪光谱可以用于模拟。此外，还证明了这是可以做到的，而实现的黑腔的峰值辐射温度的相对百分误差约为1%至4%，同时实现了19.4倍的加速。
英文摘要：
Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fidelity such that the opacity calculations can contribute ninety-five percent of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative percent error of the peak radiative temperature of the hohlraum of approximately 1\% to 4\% while achieving a 19.4x speed up.
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