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

TL;DR

This paper presents a web-based tool integrating machine learning surrogates with HPC simulations to improve student engagement and understanding in computational science courses, reducing computational costs and increasing interactivity.

Contribution

It introduces a novel ML surrogate model combined with HPC simulations and a web application for educational use, enhancing learning and accessibility.

Findings

ML surrogate predictions match explicit simulations accurately

The tool increases student engagement and understanding

Significant reduction in simulation time and computational resources

Abstract

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.