Graph Neural Network
Krishna Kumar
The University of Texas at Austin, Chishiki-AI
08/2025 (original)
In this topic, we will cover Graph Neural Networks and Graph Neural Network-based Simulators. This code requires the PyG package. If you are running on TACC using the supplied sciml container, you do not need to install PyG, since it is already in the container. If you are not running on TACC, please be aware that the installation of PyG can be a little bit tricky, and be sure to check out PyG's installation page for installation steps.
This topic includes materials that are collected together in a Jupyter notebook that can be run to reproduce the results contained in the topic pages. Access to the notebook is described in the Lab page at the end of this topic: Lab: Graph Neural Network. If you would like to run the code in the notebook as you work through the materials in this topic, consult that Lab page for information on how to proceed.
CVW material development is supported by NSF OAC awards 1854828, 2321040, 2323116 (UT Austin) and 2005506 (Indiana University)