Bulletin of the American Physical Society
APS April Meeting 2024
Wednesday–Saturday, April 3–6, 2024; Sacramento & Virtual
Session GG02: V: Nuclear Theory
11:00 AM–12:48 PM,
Friday, April 5, 2024
Room: Virtual Room 02
Sponsoring
Units:
DNP GHP
Chair: Cole Pruitt, Lawrence Livermore National Lab
Abstract: GG02.00001 : Criticality Analysis of Artifical Neural Networks in Nuclear Physics*
11:00 AM–11:12 AM
Presenter:
Simon A Sundberg
(Ohio State University)
Authors:
Simon A Sundberg
(Ohio State University)
R. J Furnstahl
(Ohio State University)
However ANNs often are treated as "black boxes", where their architecture (width, depth, and weight/bias initialization) is decided empirically based on what allows them to be trained most effectively. To actually investigate the principles of neural network architecture in a non-empirical, method based way, some have turned to performing criticality arguments with renormalization group flows in terms of the hyperparameters for weight/bias initialization and the ratio of depth to width. These criticality arguments are meant to tune these hyperparameters to give an effective theory of the neural network as an expansion in the ratio of depth to width. This allows for the study of properties of neural networks so they can have a more ideal architecture chosen from the beginning, giving a methodology to improve many projects in nuclear physics utilizing ANNs. I’ll present preliminary studies using criticality analyses geared towards nuclear physics.
*Supported in part by the NSF and the DOE
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