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.

翻译：虽然无法直接观测超浓度超短激光脉冲与近临界等离子体和超临界等离子体的相互作用,但实验性可获取量(可观测量)往往只是间接地提供关于基本等离子体动态的信息。此外,观测所提供的信息不完整,使反问题高度模糊。因此,为了推断等离子体动态和实验参数,需要考虑对参数的全面分布,要求模型具有灵活性,说明前方过程中丢失的信息。可视神经网络的设计是为了有效地模拟前向和反向过程,提供完全有条件的后方光学。在这项工作中,我们以INNs和合成电子光谱的标准统计方法为基准。首先,我们提供了接受率方面的实验结果,我们的结果显示接受率上升至10倍。此外,我们表明,这种增加的接受率还导致INNs在同样程度上加快速度。最后,我们提出了一种综合算法,利用INNs并承诺低运行时间,同时保持高精确性。