Eduardo Luis Brugnago

Eduardo Luis Brugnago
  • PhD in Physics
  • PostDoc Position at Federal University of Paraná

Dynamical Systems, Complex Systems, Neuronal Networks, Prediction Methods, Machine Learning.

About

24
Publications
1,769
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269
Citations
Introduction
Research on Dynamical Systems, Neurodynamics, particle transport, prediction methods applied to Dynamical Systems, development and implementation of Machine Learning techniques.
Current institution
Federal University of Paraná
Current position
  • PostDoc Position
Additional affiliations
December 2021 - September 2024
University of São Paulo
Position
  • PostDoc Position
Description
  • Research on particle transport, prediction methods applied to dynamical systems and implementation of machine learning techniques. Development in theoretical physics and dynamical systems using computer simulations.
Education
August 2017 - August 2021
Federal University of Paraná
Field of study
  • Physics
February 2016 - July 2017
Santa Catarina State University
Field of study
  • Physics
March 2010 - December 2015
Santa Catarina State University
Field of study
  • Physics

Publications

Publications (24)
Article
We consider an exponential parametric disturbance in the plasma production rate of the Rypdal model, and analyze the chaos emergence in this system. The perturbation modify the attractor's structure, and lead to shrimp-shaped domains in the parameter plane, where are observed periodic spirals immersed in a chaotic region. Along these periodic domai...
Article
Full-text available
In this work, effects of constant and time-dependent vaccination rates on the Susceptible–Exposed–Infected–Recovered–Susceptible (SEIRS) seasonal model are studied. Computing the Lyapunov exponent, we show that typical complex structures, such as shrimps, emerge for given combinations of a constant vaccination rate and another model parameter. In s...
Article
We investigate the synchronization of neuronal activity through a model of a clustered network formed by scale-free subnetworks, these simulating the areas of the cerebral cortex and including the spatial distribution of the vertices. The growth of the scale-free subnetworks takes place according to the fitness model, and the architecture of the cl...
Article
Full-text available
In this work, we investigate the dynamics of a discrete-time prey–predator model considering a prey reproductive response as a function of the predation risk, with the prey population growth factor governed by two parameters. The system can evolve toward scenarios of mutual or only of predators extinction, or species coexistence. We analytically sh...
Preprint
Full-text available
In this work, effects of constant and time-dependent vaccination rates on the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) seasonal model are studied. Computing the Lyapunov exponent, we show that typical complex structures, such as shrimps, emerge for given combinations of constant vaccination rate and another model parameter. In som...
Article
Full-text available
In this paper, we analyze the dynamic effect of a reservoir computer (RC) on its performance. Modified Kuramoto’s coupled oscillators are used to model the RC, and synchronization, Lyapunov spectrum (and dimension), Shannon entropy, and the upper bound of the Kolmogorov–Sinai entropy are employed to characterize the dynamics of the RC. The performa...
Article
Full-text available
We study three different strategies of vaccination in an SEIRS (Susceptible–Exposed–Infected–Recovered–Susceptible) seasonal forced model, which are ( i) continuous vaccination; ( i i) periodic short-time localized vaccination, and ( i i i) periodic pulsed width campaign. Considering the first strategy, we obtain an expression for the basic reprodu...
Article
In this work, we study the dynamics of a susceptible-exposed-infectious-recovered-susceptible epidemic model with a periodic time-dependent transmission rate. Emphasizing the influence of the seasonality frequency on the system dynamics, we analyze the largest Lyapunov exponent along parameter planes finding large chaotic regions. Furthermore, in s...
Preprint
Full-text available
In this work, we study the dynamics of a SEIRS epidemic model with a periodic time-dependent transmission rate. Emphasizing the influence of the seasonality frequency on the system dynamics, we analyze the largest Lyapunov exponent along parameter planes finding large chaotic regions. Furthermore, in some ranges there are shrimp-like periodic strut...
Article
Using the example of the city of São Paulo (Brazil), in this paper, we analyze the temporal relation between human mobility and meteorological variables with the number of infected individuals by the COVID-19 disease. For the temporal relation, we use the significant values of distance correlation t0(DC), which is a recently proposed quantity capab...
Preprint
Full-text available
Using the example of the city of São Paulo (Brazil), in this paper, we analyze the temporal relation between human mobility and meteorological variables with the number of infected individuals by the COVID-19 disease. For the temporal relation, we use the significant values of distance correlation t 0 (DC), which is a recently proposed quantity cap...
Article
Full-text available
This work considers the problem of predicting wing changes, and their duration, in systems able to support three interconnected wings (spirals) in the space of the variables. This is done by exploring the alignment of covariant Lyapunov vectors (CLVs) known to precede the occurrence of peaks and regime changes in some chaotic systems. Here, the ali...
Article
In this work, we consider a phenomenological two-dimensional discrete model coupled in a structure of a clustered network to investigate the suppression of neuronal synchronization in a complex network. We constructed a network according to a weighted human connectivity matrix and an adjacency matrix that carries small-world properties. The couplin...
Article
We investigate the synchronization of neuronal activity through a model of a clustered network formed by scale-free subnetworks, these simulating the areas of the cerebral cortex and including the spatial distribution of the vertices. The growth of the scale-free subnetworks takes place according to the fitness model, and the architecture of the cl...
Article
Oscillatory activities in the brain, detected by electroencephalograms, have identified synchronization patterns. These synchronized activities in neurons are related to cognitive processes. Additionally, experimental research studies on neuronal rhythms have shown synchronous oscillations in brain disorders. Mathematical modeling of networks has b...
Article
We show that a characteristic alignment between Lyapunov vectors can be used to predict regime changes as well as regime duration in the classical Lorenz model of atmospheric convection. By combining Lyapunov vector alignment with maxima in the local expansion of bred vectors, we obtain an effective and competitive method to significantly decrease...
Article
In this paper, the alignment of covariant Lyapunov vectors is used to train multi-layer perceptron ensembles in order to predict the duration of regimes in chaotic time series of Rikitake’s geomagnetic dynamo model. The machine learning procedure reveals the relevance of the alignment of distinct covariant Lyapunov vectors for the predictions. To t...
Article
The cumulative number of confirmed infected individuals by the new coronavirus outbreak until April 30th, 2020, is presented for the countries: Belgium, Brazil, United Kingdom (UK), and the United States of America (USA). After an initial period with a low incidence of newly infected people, a power-law growth of the number of confirmed cases is ob...
Preprint
Full-text available
The cumulative number of confirmed infected individuals by the new coronavirus outbreak until April 30th, 2020, is presented for the countries: Belgium, Brazil, United Kingdom (UK), and United States of America (USA). After an initial period with a low incidence of newly infected people, a power-law growth of the number of confirmed cases is observ...
Article
In this paper, we use machine learning strategies aiming to predict chaotic time series obtained from the Lorenz system. Such strategies prove to be successful in predicting the evolution of dynamical variables over a short period of time. Transitions between the regimes and their duration can be predicted with great accuracy by means of counting a...
Article
Full-text available
In this work, we analyze the growth of the cumulative number of confirmed infected cases by a novel coronavirus (COVID-19) until March 27, 2020, from countries of Asia, Europe, North America, and South America. Our results show that (i) power-law growth is observed in all countries; (ii) by using the distance correlation, the power-law curves betwe...
Preprint
Full-text available
In this work we analyse the growth of the cumulative number of confirmed infected cases by the COVID-19 until March 27th, 2020, from countries of Asia, Europe, North and South America. Our results show (i) that power-law growth is observed for all countries; (ii) that the distance correlation of the power-law curves between countries are statistica...
Article
We study a pair of nonlinearly coupled identical chaotic sine square maps. More specifically, we investigate the chaos suppression associated with the variation of two parameters. Two-dimensional parameter-space regions where the chaotic dynamics of the individual chaotic sine square map is driven towards regular dynamics are delimited. Additionall...

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