  Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy
Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma.
Systematic constraints on these properties must combine measurements from
different collision systems and methodically account for experimental and
theoretical uncertainties. Such studies require a vast number of costly
numerical simulations. While computationally inexpensive surrogate models
("emulators") can be used to efficiently approximate the predictions of heavy
ion simulations across a broad range of model parameters, training a reliable
emulator remains a computationally expensive task. We use transfer learning to
map the parameter dependencies of one model emulator onto another, leveraging
similarities between different simulations of heavy ion collisions. By limiting
the need for large numbers of simulations to only one of the emulators, this
technique reduces the numerical cost of comprehensive uncertainty
quantification when studying multiple collision systems and exploring different
models.
