التفاصيل البيبلوغرافية
| العنوان: |
Symmetry Group Equivariant Architectures for Physics |
| المؤلفون: |
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, Miller, David W., Daniel Murnane, Jan Tuzlić Offermann, Mariel Pettee, Phiala Shanahan, Chase Shimmin, Savannah Thais |
| المصدر: |
Jan Tuzlić Offermann |
| بيانات النشر: |
arXiv, 2022. |
| سنة النشر: |
2022 |
| مصطلحات موضوعية: |
FOS: Computer and information sciences, Computer Science - Machine Learning, High Energy Physics - Phenomenology, High Energy Physics - Experiment (hep-ex), Artificial Intelligence (cs.AI), High Energy Physics - Phenomenology (hep-ph), Computer Science - Artificial Intelligence, FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), High Energy Physics - Experiment, Machine Learning (cs.LG) |
| الوصف: |
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. In this report, we argue that both the physics community and the broader machine learning community have much to understand and potentially to gain from a deeper investment in research concerning symmetry group equivariant machine learning architectures. For some 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). We discuss various figures of merit for evaluating these models as well as some potential benefits and limitations of these methods for a variety of physics applications. Research and investment into these approaches will lay the foundation for future architectures that are potentially more robust under new computational paradigms and will provide a richer description of the physical systems to which they are applied.
Comment: Contribution to Snowmass 2021 |
| DOI: |
10.48550/arxiv.2203.06153 |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d97018db7867269ab7971f4186d23d3 |
| Rights: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....0d97018db7867269ab7971f4186d23d3 |
| قاعدة البيانات: |
OpenAIRE |