We report on advances to interpret current and future gravitational-wave events in light of astrophysical simulations .
A machine-learning emulator is trained on numerical population-synthesis predictions and inserted into a Bayesian hierarchical framework .
In this case study , a modest but state-of-the-art suite of simulations of isolated binary stars is interpolated across two event parameters and one population parameter .
The validation process of our pipelines highlights how omitting some of the event parameters might cause errors in estimating selection effects , which propagates as systematics to the final population inference .
Using LIGO/Virgo data from O1 and O2 we infer that black holes in binaries are most likely to receive natal kicks with one-dimensional velocity dispersion \sigma = 105 ^ { +44 } _ { -29 } km / s .
Our results showcase potential applications of machine-learning tools in conjunction with population-synthesis simulations and gravitational-wave data .