000161867 001__ 161867
000161867 005__ 20251017144616.0
000161867 0247_ $$2doi$$a10.1088/2632-2153/ade04d
000161867 0248_ $$2sideral$$a144470
000161867 037__ $$aART-2025-144470
000161867 041__ $$aeng
000161867 100__ $$0(orcid)0000-0002-8371-9840$$aCalvo-Barlés, Pablo
000161867 245__ $$aMachine learning for detection of equivariant finite symmetry groups in dynamical systems
000161867 260__ $$c2025
000161867 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161867 5203_ $$aIn this work, we introduce the equivariance seeker model (ESM), a data-driven method for discovering the underlying finite equivariant symmetry group of an arbitrary function. ESM achieves this by optimizing a loss function that balances equivariance preservation with the penalization of redundant solutions, ensuring the complete and accurate identification of all symmetry transformations. We apply this framework specifically to dynamical systems, identifying their symmetry groups directly from observed trajectory data. To demonstrate its versatility, we test ESM on multiple systems in two distinct scenarios: (i) when the governing equations are known theoretically and (ii) when they are unknown, and the equivariance finding relies solely on observed data. The latter case highlights ESM’s fully data-driven capability, as it requires no prior knowledge of the system’s equations to operate.
000161867 536__ $$9info:eu-repo/grantAgreement/ES/DGA/Q-MAD$$9info:eu-repo/grantAgreement/ES/MCIU/PID2023-148359NB-C21$$9info:eu-repo/grantAgreement/ES/MICIU/CEX2023-001286-S
000161867 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000161867 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000161867 700__ $$0(orcid)0000-0001-6575-168X$$aGutierrez Rodrigo, Sergio$$uUniversidad de Zaragoza
000161867 700__ $$0(orcid)0000-0001-9273-8165$$aMartín-Moreno, Luis
000161867 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000161867 773__ $$g6, 2 (2025), 025058 [20 pp.]$$tMachine Learning: Science and Technology$$x2632-2153
000161867 8564_ $$s1946642$$uhttps://zaguan.unizar.es/record/161867/files/texto_completo.pdf$$yVersión publicada
000161867 8564_ $$s672826$$uhttps://zaguan.unizar.es/record/161867/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000161867 909CO $$ooai:zaguan.unizar.es:161867$$particulos$$pdriver
000161867 951__ $$a2025-10-17-14:19:28
000161867 980__ $$aARTICLE