A machine learning approach to predicting dynamical observables from network structure (2024)
- Authors:
- USP affiliated authors: RODRIGUES, FRANCISCO APARECIDO - ICMC ; PERON, THOMAS KAUÊ DAL'MASO - ICMC
- Unidade: ICMC
- DOI: 10.1098/rspa.2024.0435
- Subjects: REDES COMPLEXAS; APRENDIZADO COMPUTACIONAL; SISTEMAS DINÂMICOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings of the Royal Society A
- ISSN: 1471-2946
- Volume/Número/Paginação/Ano: v. 481, p. 1-12, 2024
- Status:
- Artigo aberto em periódico híbrido (Hybrid Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
RODRIGUES, Francisco Aparecido et al. A machine learning approach to predicting dynamical observables from network structure. Proceedings of the Royal Society A, v. 481, p. 1-12, 2024Tradução . . Disponível em: https://doi.org/10.1098/rspa.2024.0435. Acesso em: 30 mar. 2026. -
APA
Rodrigues, F. A., Peron, T., Connaughton, C., Kurths, J., & Moreno, Y. (2024). A machine learning approach to predicting dynamical observables from network structure. Proceedings of the Royal Society A, 481, 1-12. doi:10.1098/rspa.2024.0435 -
NLM
Rodrigues FA, Peron T, Connaughton C, Kurths J, Moreno Y. A machine learning approach to predicting dynamical observables from network structure [Internet]. Proceedings of the Royal Society A. 2024 ; 481 1-12.[citado 2026 mar. 30 ] Available from: https://doi.org/10.1098/rspa.2024.0435 -
Vancouver
Rodrigues FA, Peron T, Connaughton C, Kurths J, Moreno Y. A machine learning approach to predicting dynamical observables from network structure [Internet]. Proceedings of the Royal Society A. 2024 ; 481 1-12.[citado 2026 mar. 30 ] Available from: https://doi.org/10.1098/rspa.2024.0435 - Collective dynamics of random Janus oscillator networks
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