  Applications of new techniques in machine learning are speeding up progress
in research in various fields. In this work, we construct and evaluate a deep
neural network (DNN) to be used within a Bayesian statistical framework as a
faster and more reliable alternative to the Gaussian Process (GP) emulator of
an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model
simulator of heavy-ion reactions at intermediate beam energies. We found strong
evidence of DNN being able to emulate the IBUU simulator's prediction on the
strengths of protons' directed and elliptical flow very efficiently even with
small training datasets and with accuracy about ten times higher than the GP.
Limitations of our present work and future improvements are also discussed.
