With the upcoming HL-LHC, the budget for computing will be insufficient to generate a sufficient amount of Monte-Carlo events for both signal and background predictions. The driving force behind these costs is the inefficiency of the Monte-Carlo phase space generators and the unweighting efficiencies.
After a short review of traditional algorithms, I will introduce a new Machine Learning algorithm that uses Normalizing Flows for efficient numerical integration and random sampling. This approach is especially efficient in high-dimensional integration spaces. I will show some preliminary results obtained with the matrix element generator of Sherpa and discuss different choices of hyperparameters and their influence on the result.