论文标题
转移学习作为在模拟中重现高保真性NLTE不熟悉的一种方法
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations
论文作者
论文摘要
高能密度物理学的模拟通常需要非本地热力学平衡（NLTE）不透明度数据。但是，这些数据以相对较低的结果生产成本。在高保真度中，它甚至更大，因此不透明度计算可以贡献总计算时间的百分之九十五。这一比例甚至可以达到很大的比例。神经网络可用于替换低保真数据的标准计算，并且可以训练神经网络以重现人工，高保真的不透明光谱。在这项工作中，证明了一种新型的神经网络体系结构，该结构训练了通过转移学习来复制高保真krypton光谱的训练。此外，可以证明这可以在达到大约1 \％至4 \％的峰值辐射温度的相对百分比时完成，同时达到19.4倍的速度。
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.
