Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning
Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning
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DOI：
10.1109/eduhpc51895.2020.00009
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
V. Jadhao;J. Kadupitiya
中科院分区：
文献类型：
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作者：
V. Jadhao;J. Kadupitiya
我们探讨了将机器学习（ML）与高性能计算（HPC）驱动的模拟相结合的想法，以解决使用模拟来教授计算科学和工程课程的挑战。我们证明了一个ML代理，使用人工神经网络设计，产生预测与显式模拟非常一致，但在更少的时间和计算成本。我们在nanoHUB上开发了一个Web应用程序，该应用程序支持HPC驱动的模拟和ML代理方法来生成模拟输出。该工具用于课堂教学和解决与两门课程相关的家庭作业问题，这两门课程涵盖了计算材料科学，建模和仿真以及HPC启用仿真的工程应用等广泛领域的主题。通过在课堂上的学生反馈和调查的工具的评价表明，ML增强的工具提供了一个动态的和响应式的模拟环境，提高学生的学习。在实时参与和随时访问方面与仿真框架的交互性的改进使学生能够通过快速可视化输出量随输入变化的变化来发展对物理系统行为的直觉。
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.