Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T5: Graph Neural Networks |
Hide Abstracts |
Chair: William Ratcliff, National Institute of Standards and Technology Room: Room 129 |
Sunday, March 5, 2023 1:30PM - 2:30PM |
T5.00001: Graph Neural Networks Savannah J Thais Graph neural networks (GNNs) have become popular tools for processing physics data. A GNN is a neural network that takes as input a graph object composed of nodes, edges, and global features and outputs another graph, which could be a single global feature in the case of binary classification. Many physics datasets can be most naturally represented as a graph or a point cloud and so GNNs may be the most effective deep learning tool to analyze them. These tools can encode the geometry of our complex data without requiring a regular grid and also respect other aspects of the data structure such as permutation invariance, symmetries, variable size, etc. These tools have a range of applicability including materials discovery, clustering, image segmentation, particle tracking, etc. The goal of this tutorial is to provide a hands-on introduction to GNNs for physicists by physicists. |
Sunday, March 5, 2023 2:30PM - 3:30PM |
T5.00002: Graph Neural Networks Ekin D Cubuk Graph neural networks (GNNs) have become popular tools for processing physics data. A GNN is a neural network that takes as input a graph object composed of nodes, edges, and global features and outputs another graph, which could be a single global feature in the case of binary classification. Many physics datasets can be most naturally represented as a graph or a point cloud and so GNNs may be the most effective deep learning tool to analyze them. These tools can encode the geometry of our complex data without requiring a regular grid and also respect other aspects of the data structure such as permutation invariance, symmetries, variable size, etc. These tools have a range of applicability including materials discovery, clustering, image segmentation, particle tracking, etc. The goal of this tutorial is to provide a hands-on introduction to GNNs for physicists by physicists. |
Sunday, March 5, 2023 3:30PM - 4:30PM |
T5.00003: Graph Neural Networks Kamal Choudhary Graph neural networks (GNNs) have become popular tools for processing physics data. A GNN is a neural network that takes as input a graph object composed of nodes, edges, and global features and outputs another graph, which could be a single global feature in the case of binary classification. Many physics datasets can be most naturally represented as a graph or a point cloud and so GNNs may be the most effective deep learning tool to analyze them. These tools can encode the geometry of our complex data without requiring a regular grid and also respect other aspects of the data structure such as permutation invariance, symmetries, variable size, etc. These tools have a range of applicability including materials discovery, clustering, image segmentation, particle tracking, etc. The goal of this tutorial is to provide a hands-on introduction to GNNs for physicists by physicists. |
Sunday, March 5, 2023 4:30PM - 5:30PM |
T5.00004: Graph Neural Networks Brian DeCost Graph neural networks (GNNs) have become popular tools for processing physics data. A GNN is a neural network that takes as input a graph object composed of nodes, edges, and global features and outputs another graph, which could be a single global feature in the case of binary classification. Many physics datasets can be most naturally represented as a graph or a point cloud and so GNNs may be the most effective deep learning tool to analyze them. These tools can encode the geometry of our complex data without requiring a regular grid and also respect other aspects of the data structure such as permutation invariance, symmetries, variable size, etc. These tools have a range of applicability including materials discovery, clustering, image segmentation, particle tracking, etc. The goal of this tutorial is to provide a hands-on introduction to GNNs for physicists by physicists. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700