An analysis of network automata as models for biological and natural processes

• Grant number: 21/08325-2
• Support Opportunities: Regular Research Grants
• Duration: January 01, 2022 - December 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Convênio/Acordo: Research Foundation - Flanders (FWO)
• Principal Investigator: Odemir Martinez Bruno
• Grantee: Odemir Martinez Bruno
• Principal researcher abroad: Jan Marcel Baetens
• Institution abroad: Ghent University (UGent), Belgium
• Principal researcher abroad: Luis Enrique Correa da Rocha
• Institution abroad: Ghent University (UGent), Belgium
• Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 23/07241-5 - Combining network-automata and neural networks for data analysis, BP.PD

Abstract

Increasing computational power allows for the description and prediction of natural processes by modelling its smallest-scale interactions, recreating natural phenomena in a bottom-up fashion. Two classes of such approaches, which are especially pertinent for modelling spatio-temporal processes, are cellular automata (CAs) and their topological extension, network automata (NAs). The simplest types of CAs and NAs are well studied, but for these models to acquire the status of a strong scientific paradigm, they must allow for simple model extensions, whilst at the same time remaining methodologically robust. We will extend these computational models by introducing "spatial heterogeneity", which includes "rule heterogeneity" and "topological heterogeneity". The former translates to extending the model in such a way that the set of rules determining the microscopic dynamics, and therefore the emergent process, must no longer be the same on every spatially distinct region. The latter, which is exclusive to NAs, translates to allowing different types of connections between nodes. In both cases, a mathematical framework for these heterogeneities as well as a methodological enquiry into their effect on model stability, quantified by discrete extensions of the Lyapunov exponent, are at the core of this proposal. In a final stage, we will mobilize these models and the newly developed analytical techniques in two applications: epidemiology and opinion dynamics. (AU)

Publicações científicas

(Referências obtidas automaticamente do Web of Science e do SciELO, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores)

VALLE, JOAO; MACHICAO, JEANETH; BRUNO, ODEMIR M.. Chaotical PRNG based on composition of logistic and tent maps using deep-zoom. CHAOS SOLITONS & FRACTALS, v. 161, p. 10-pg., 2022-06-27. (21/07377-9, 21/08325-2, 18/22214-6, 22/01935-2, 20/03514-9)

SCABINI, LEONARDO; ZIELINSKI, KALLIL M.; RIBAS, LUCAS C.; GONCALVES, WESLEY N.; DE BAETS, BERNARD; BRUNO, ODEMIR M.. RADAM: Texture recognition through randomized aggregated encoding of deep activation maps. PATTERN RECOGNITION, v. 143, p. 13-pg., 2023-11-01. (22/03668-1, 21/09163-6, 18/22214-6, 21/07289-2, 21/08325-2, 19/07811-0)

SCABINI, LEONARDO F. S.; BRUNO, ODEMIR M.. Structure and performance of fully connected neural networks: Emerging complex network properties. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 615, p. 17-pg., 2023-03-03. (21/08325-2, 19/07811-0)