arXiv Open Access 2023

Finding discrete symmetry groups via Machine Learning

Pablo Calvo-Barlés Sergio G. Rodrigo Eduardo Sánchez-Burillo Luis Martín-Moreno

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Abstrak

We introduce a machine-learning approach (denoted Symmetry Seeker Neural Network) capable of automatically discovering discrete symmetry groups in physical systems. This method identifies the finite set of parameter transformations that preserve the system's physical properties. Remarkably, the method accomplishes this without prior knowledge of the system's symmetry or the mathematical relationships between parameters and properties. Demonstrating its versatility, we showcase examples from mathematics, nanophotonics, and quantum chemistry.

Topik & Kata Kunci

physics.comp-ph physics.chem-ph physics.optics quant-ph

Penulis (4)

Pablo Calvo-Barlés

Sergio G. Rodrigo

Eduardo Sánchez-Burillo

Luis Martín-Moreno

Format Sitasi

APA MLA BibTeX

Calvo-Barlés, P., Rodrigo, S.G., Sánchez-Burillo, E., Martín-Moreno, L. (2023). Finding discrete symmetry groups via Machine Learning. https://arxiv.org/abs/2307.13457

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓