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While the interaction of ultra-intense ultra-short laser pulses with near-and overcritical plasmas cannot be directly observed, experimentally accessiblequantities (observables) often only indirectly give information about theunderlying plasma dynamics. Furthermore, the information provided byobservables 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 theforward process. Invertible Neural Networks (INNs) have been designed toefficiently model both the forward and inverse process, providing the fullconditional posterior given a specific measurement. In this work, we benchmarkINNs and standard statistical methods on synthetic electron spectra. First, weprovide experimental results with respect to the acceptance rate, where ourresults show increases in acceptance rates up to a factor of 10. Additionally,we show that this increased acceptance rate also results in an increasedspeed-up for INNs to the same extent. Lastly, we propose a composite algorithmthat utilizes INNs and promises low runtimes while preserving high accuracy.

Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparison