Deep Learning of the Biswas–Chatterjee–Sen Model
We investigate the critical properties of kinetic continuous opinion dynamics using deep learning techniques. The system consists of <i>N</i> continuous spin variables in the interval [-1,1]. Dense neural networks are trained on spin configuration data generated via kinetic Monte Carlo simulations, accurately identifying the critical point on both square and triangular lattices. Classical unsupervised learning with principal component analysis reproduces the magnetization and allows estimation of critical exponents. Additionally, variational autoencoders are implemented to study the phase transition through the loss function, which behaves as an order parameter. A correlation function between real and reconstructed data is defined and found to be universal at the critical point.
Authors
- D.S.M. Alencar (ORCID: https://orcid.org/0000-0002-0549-8827)
- T. F. A. Alves (ORCID: https://orcid.org/0000-0001-9937-7980)
- Ronan S. Ferreira (ORCID: https://orcid.org/0000-0002-7623-1361)
- A. Macedo-Filho (ORCID: https://orcid.org/0000-0002-9419-5595)
- José F. S. Neto (ORCID: https://orcid.org/0000-0001-5738-9895)
- G. A. Alves (ORCID: https://orcid.org/0000-0002-6934-005X)
- F. W. S. Lima (ORCID: https://orcid.org/0000-0003-0895-4186)
- L. Brito (ORCID: https://orcid.org/0009-0006-3890-0495)
Institutions
- Universidade Federal de Ouro Preto (BR)
- Universidade Federal do Piauí (BR)
- Universidade Estadual do Piauí (BR)
- Universidade Estadual do Maranhão (BR)
Publication Details
- Journal
- Entropy
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3390/e27111173
- Citations
- 1
- Primary Topic
- Quantum many-body systems
- Type
- article
- Field-Weighted Citation Impact
- 1.25
Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Financiadora de Estudos e Projetos
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- Fundação de Amparo à Pesquisa do Estado do Piauí