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We explore the idea of integrating machine learning (ML) with highperformance computing (HPC)-driven simulations to address challenges in usingsimulations to teach computational science and engineering courses. Wedemonstrate that a ML surrogate, designed using artificial neural networks,yields predictions in excellent agreement with explicit simulation, but at farless time and computing costs. We develop a web application on nanoHUB thatsupports both HPC-driven simulation and the ML surrogate methods to producesimulation outputs. This tool is used for both in-classroom instruction and forsolving homework problems associated with two courses covering topics in thebroad areas of computational materials science, modeling and simulation, andengineering applications of HPC-enabled simulations. The evaluation of the toolvia in-classroom student feedback and surveys shows that the ML-enhanced toolprovides a dynamic and responsive simulation environment that enhances studentlearning. The improvement in the interactivity with the simulation framework interms of real-time engagement and anytime access enables students to developintuition for the physical system behavior through rapid visualization ofvariations in output quantities with changes in inputs.

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning