Tânia Tomé

Stochastic lattice gas model for a predator-prey system

We propose a stochastic lattice gas model to describe the dynamics of two animal species population, one being a predator and the other a prey. This model comprehends the mechanisms of the Lotka-Volterra model. Our analysis was performed by using a dynamical mean-field approximation and computer simulations. Our results show that the system exhibits an oscillatory behavior of the population densities of prey and predators. For the sets of parameters used in our computer simulations, these oscillations occur at a local level. Mean-field results predict synchronized collective oscillations.

Entropy Production in Nonequilibrium Systems Described by a Fokker-Planck Equation

We study the entropy production in nonequilibrium systems described by a Fokker-Planck equation. We have devised an expression for the entropy flux in the stationary state. We have found that the entropy flux can be written as an ensemble average of an expression containing the force and its derivative. This result is similar to the one used by Lebowitz and Spohn for system following a Markovian process in discrete space. We have also been able to obtain a fluctuation-dissipation type relation between the dissipated power, which was written as an ensemble average, and the production of entropy for the case of systems in contact with one heat bath. We have applied the results for a simple model for particles subjected to dissipative forces.

Stochastic lattice gas model describing the dynamics of the SIRS epidemic process

We study a stochastic process describing the onset of spreading dynamics of an epidemic in a population composed of individuals of three classes: susceptible (S), infected (I), and recovered (R). The stochastic process is defined by local rules and involves the following cyclic process: S → I → R → S (SIRS). The open process S → I → R (SIR) is studied as a particular case of the SIRS process. The epidemic process is analyzed at different levels of description: by a stochastic lattice gas model and by a birth and death process. By means of Monte Carlo simulations and dynamical mean-field approximations we show that the SIRS stochastic lattice gas model exhibit a line of critical points separating the two phases: an absorbing phase where the lattice is completely full of S individuals and an active phase where S, I and R individuals coexist, which may or may not present population cycles. The critical line, that corresponds to the onset of epidemic spreading, is shown to belong in the directed percolation universality class. By considering the birth and death process we analyze the role of noise in stabilizing the oscillations.

The threshold of coexistence and critical behaviour of a predator–prey cellular automaton

We study a probabilistic cellular automaton to describe two population biology problems: the threshold of species coexistence in a predator–prey system and the spreading of an epidemic in a population. By carrying out mean-field approximations and numerical simulations we obtain the phase boundaries (thresholds) related to the transition between an active state, where prey and predators present a stable coexistence, and a prey absorbing state. The numerical estimates for the critical exponents show that the transition belongs to the directed percolation universality class. In the limit where the cellular automaton maps into a model for the spreading of an epidemic with immunization we observe a crossover from directed percolation class to the dynamic percolation class. Patterns of growing clusters related to species coexistence and spreading of epidemic are shown and discussed.

Spreading of damage in the Domany-Kinzel cellular automaton: A mean-field approach

We present a detailed analytical formulation and a mean-field approximation analysis of the spreading of damage in the Domany-Kinzel cellular automaton. Our results show that the system exhibits a chaotic state besides the frozen and active. These results are in agreement with recent numerical simulations. Also we study the conjugate fields associated to the order parameters of the active and chaotic phases.

Stochastic dynamics and irreversibility

Random Variables.- Sequence of Independent Variables.- Langevin equation.- Fokker-Planck Equation I.- Fokker-Planck Equation II.- Markov Chains.- Master Equation I.- Master Equation II.- Phase Transitions and Criticality.- Reactive Systems.- Glauber Model.- Systems with Inversion Symmetry.- Systems with Absorbing States.- Population Dynamics.- Probabilistic Cellular automata.- Reaction-Diffusion Processes.- Random Sequential Adsoprtion.- Percolation.

Critical behavior of the susceptible-infected-recovered model on a square lattice

By means of numerical simulations and epidemic analysis, the transition point of the stochastic asynchronous susceptible-infected-recovered model on a square lattice is found to be c0=0.1765005(10), where c is the probability a chosen infected site spontaneously recovers rather than tries to infect one neighbor. This point corresponds to an infection/recovery rate of λ(c)=(1-c0)/c0=4.665 71(3) and a net transmissibility of (1-c0)/(1+3c0)=0.538 410(2), which falls between the rigorous bounds of the site and bond thresholds. The critical behavior of the model is consistent with the two-dimensional percolation universality class, but local growth probabilities differ from those of dynamic percolation cluster growth, as is demonstrated explicitly.

Stochastic approach to equilibrium and nonequilibrium thermodynamics.

We develop the stochastic approach to thermodynamics based on stochastic dynamics, which can be discrete (master equation) and continuous (Fokker-Planck equation), and on two assumptions concerning entropy. The first is the definition of entropy itself and the second the definition of entropy production rate, which is non-negative and vanishes in thermodynamic equilibrium. Based on these assumptions, we study interacting systems with many degrees of freedom in equilibrium or out of thermodynamic equilibrium and how the macroscopic laws are derived from the stochastic dynamics. These studies include the quasiequilibrium processes; the convexity of the equilibrium surface; the monotonic time behavior of thermodynamic potentials, including entropy; the bilinear form of the entropy production rate; the Onsager coefficients and reciprocal relations; and the nonequilibrium steady states of chemical reactions.

Glassy states in lattice models with many coexisting crystalline phases

We study the emergence of glassy states after a sudden cooling in lattice models with short-range interactions and without any a priori quenched disorder. The glassy state emerges whenever the equilibrium model possesses a sufficient number of coexisting crystalline phases at low temperatures, provided the thermodynamic limit be taken before the infinite time limit. This result is obtained through simulations of the time relaxation of the standard Potts model and some exclusion models equipped with a local stochastic dynamics on a square lattice.

Stable oscillations of a predator-prey probabilistic cellular automaton : a mean-field approach

We analyze a probabilistic cellular automaton describing the dynamics of coexistence of a predator–prey system. The individuals of each species are localized over the sites of a lattice and the local stochastic updating rules are inspired by the processes of the Lotka–Volterra model. Two levels of mean-field approximations are set up. The simple approximation is equivalent to an extended patch model, a simple metapopulation model with patches colonized by prey, patches colonized by predators and empty patches. This approximation is capable of describing the limited available space for species occupancy. The pair approximation is moreover able to describe two types of coexistence of prey and predators: one where population densities are constant in time and another displaying self-sustained time oscillations of the population densities. The oscillations are associated with limit cycles and arise through a Hopf bifurcation. They are stable against changes in the initial conditions and, in this sense, they differ from the Lotka–Volterra cycles which depend on initial conditions. In this respect, the present model is biologically more realistic than the Lotka–Volterra model.