论文标题
近临界激光 - 比较的电子光谱上可逆神经网络的接受率：比较
Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparison
论文作者
论文摘要
虽然无法直接观察到超明显的超短激光脉冲与接近和临界等离子体的相互作用，但实验可访问的数量（可观察到）通常仅间接地提供有关潜在等离子体动力学的信息。此外，可观察物提供的信息是不完整的，这使得反问题高度模棱两可。因此，为了推断等离子动力学和实验参数，需要考虑的参数上的完整分布需要考虑，要求模型具有灵活性并说明在正向过程中丢失的信息。可逆的神经网络（INNS）旨在有效地对前和反向过程进行建模，并在特定的测量中提供了完整的条件后部。在这项工作中，我们基于合成电子光谱基准和标准统计方法。首先，我们提供有关接受率的实验结果，在此结果表明，接受率的提高最高为10倍。此外，我们表明，这种增加的接受率也导致Inns的加速速度在同一程度上增加。最后，我们提出了一种复合算法，该算法利用Inns并承诺在保持较高精度的同时，可以使用较低的运行时间。
While the interaction of ultra-intense ultra-short laser pulses with near- and overcritical plasmas cannot be directly observed, experimentally accessible quantities (observables) often only indirectly give information about the underlying plasma dynamics. Furthermore, the information provided by observables is incomplete, making the inverse problem highly ambiguous. Therefore, in order to infer plasma dynamics as well as experimental parameter, the full distribution over parameters given an observation needs to considered, requiring that models are flexible and account for the information lost in the forward process. Invertible Neural Networks (INNs) have been designed to efficiently model both the forward and inverse process, providing the full conditional posterior given a specific measurement. In this work, we benchmark INNs and standard statistical methods on synthetic electron spectra. First, we provide experimental results with respect to the acceptance rate, where our results show increases in acceptance rates up to a factor of 10. Additionally, we show that this increased acceptance rate also results in an increased speed-up for INNs to the same extent. Lastly, we propose a composite algorithm that utilizes INNs and promises low runtimes while preserving high accuracy.
