Bulletin of the American Physical Society
APS April Meeting 2023
Volume 68, Number 6
Minneapolis, Minnesota (Apr 1518)
Virtual (Apr 2426); Time Zone: Central Time
Session D04: Equivariance Meets Covariance: Physicsinformed Machine LearningInvited

Hide Abstracts 
Sponsoring Units: GDS Chair: Mariel Pettee, Lawrence Berkeley National Lab Room: MG Salon C  3rd Floor 
Saturday, April 15, 2023 3:45PM  4:21PM 
D04.00001: Equivariance Meets Covariance: Physicsinformed Machine Learning Invited Speaker: Mariel Pettee Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of machine learning, an awareness of symmetries such as rotation or permutation invariance has driven impressive performance breakthroughs in computer vision, natural language processing, and other important applications. For some ML applications, the introduction of symmetries into the fundamental structural design can yield models that are more economical (i.e. contain fewer, but more expressive, learned parameters), interpretable (i.e. more explainable or directly mappable to physical quantities), and/or trainable (i.e. more efficient in both data and computational requirements). Here, I'll provide an overview of some of the latest applications of equivariance & covariance in physicsinformed ML. 
Saturday, April 15, 2023 4:21PM  4:57PM 
D04.00002: Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis Invited Speaker: Biwei Dai In this talk we introduce Translation and Rotation Equivariant Normalizing Flow (TRENF), a generative Normalizing Flow (NF) model which explicitly incorporates translation and rotation symmetries. We apply TRENF to cosmological data analysis, where TRENF allows direct access to the high dimensional data likelihood p(xy) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On nonlinear cosmological overdensity fields from Nbody simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. 
Saturday, April 15, 2023 4:57PM  5:33PM 
D04.00003: Equivariant Neural Networks for Particle Physics: PELICAN Invited Speaker: Alexander Bogatskiy We hold these truths to be selfevident: that all physics problems are created unequal, that they are endowed with their unique data structures and symmetries, that among these are tensor transformation laws, Lorentz symmetry, and permutation equivariance. A lot of attention has been paid to the applications of common machine learning methods in physics experiments and theory. However, much less attention is paid to the methods themselves and their viability as physics modeling tools. One of the most fundamental aspects of modeling physical phenomena is the identification of the symmetries that govern them. Incorporating symmetries into a model can reduce the risk of overparameterization, and consequently improve a model's robustness and predictive power. Building off of previous work, I will demonstrate how careful choices in the details of network design – creating a model both simpler and more grounded in physics than the traditional approaches – can yield stateoftheart performance despite the symmetry constraints. I will describe the PermutationEquivariant and LorentzInvariant or Covariant Aggregator Network (PELICAN), which is based on a fusion of classical ideas from Invariant Theory and recent work on permutationequivariant maps. As a proof of concept, it is applied to tagging and novel reconstruction (regression) problems. Particular attention will be paid to the remarkable explainability features of this kind of architecture, made possible only by the implementation of both permutation and full Lorentz symmetries. In particular, constituentbased regression with PELICAN results in particlelevel features that can be visualized and interpreted in ways impossible with any traditional architecture. 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit 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 207403844
(301) 2093200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700