自动编码器和代理模型中高保真不透明度光谱的迁移学习
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models
摘要：
高能量密度物理学的模拟是昂贵的，主要部分是因为需要产生非局部热力学平衡不透明度。高保真度光谱可能会在模拟中揭示低保真度光谱所看不到的新物理，但这些模拟的成本也会随着所使用的不透明度的保真度水平而变化。神经网络能够再现这些光谱，但是神经网络需要数据来训练它们，这限制了训练数据的保真度水平。本文证明，通过对用低保真度数据训练的神经网络进行迁移学习，可以用50个高保真氪数据样本再现中位误差在3 ~ 4%范围内的高保真光谱。
英文摘要：
Simulations of high energy density physics are expensive, largely in part for the need to produce non-local thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not seen with low-fidelity spectra, but the cost of these simulations also scale with the level of fidelity of the opacities being used. Neural networks are capable of reproducing these spectra, but neural networks need data to to train them which limits the level of fidelity of the training data. This paper demonstrates that it is possible to reproduce high-fidelity spectra with median errors in the realm of 3\% to 4\% using as few as 50 samples of high-fidelity Krypton data by performing transfer learning on a neural network trained on many times more low-fidelity data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
