  For simulations where the forward and the inverse directions have a physics
meaning, invertible neural networks are especially useful. A conditional INN
can invert a detector simulation in terms of high-level observables,
specifically for ZW production at the LHC. It allows for a per-event
statistical interpretation. Next, we allow for a variable number of QCD jets.
We unfold detector effects and QCD radiation to a pre-defined hard process,
again with a per-event probabilistic interpretation over parton-level phase
space.
