  We explore the idea of integrating machine learning (ML) with high
performance computing (HPC)-driven simulations to address challenges in using
simulations to teach computational science and engineering courses. We
demonstrate that a ML surrogate, designed using artificial neural networks,
yields predictions in excellent agreement with explicit simulation, but at far
less time and computing costs. We develop a web application on nanoHUB that
supports both HPC-driven simulation and the ML surrogate methods to produce
simulation outputs. This tool is used for both in-classroom instruction and for
solving homework problems associated with two courses covering topics in the
broad areas of computational materials science, modeling and simulation, and
engineering applications of HPC-enabled simulations. The evaluation of the tool
via in-classroom student feedback and surveys shows that the ML-enhanced tool
provides a dynamic and responsive simulation environment that enhances student
learning. The improvement in the interactivity with the simulation framework in
terms of real-time engagement and anytime access enables students to develop
intuition for the physical system behavior through rapid visualization of
variations in output quantities with changes in inputs.
