  We introduce a model of hadronization based on invertible neural networks
that faithfully reproduces a simplified version of the Lund string model for
meson hadronization. Additionally, we introduce a new training method for
normalizing flows, termed MAGIC, that improves the agreement between simulated
and experimental distributions of high-level (macroscopic) observables by
adjusting single-emission (microscopic) dynamics. Our results constitute an
important step toward realizing a machine-learning based model of hadronization
that utilizes experimental data during training. Finally, we demonstrate how a
Bayesian extension to this normalizing-flow architecture can be used to provide
analysis of statistical and modeling uncertainties on the generated observable
distributions.
