Temporal Entropy Evolution in Stochastic and Delayed Dynamics

Ergodic stationary distribution and extinction of stochastic delay chemostat system with Monod–Haldane functional response and higher-order Lévy jumps

Adaptive fault tolerant tracking control for a class of stochastic nonlinear systems with output constraint and actuator faults

In order to accelerate computations and improve long time accuracy of numerical simulations, this thesis develops multiscale geometric integrators. For general multiscale stiff ODEs, SDEs, and PDEs, FLow AVeraging integratORs (FLAVORs) have been proposed for the coarse time-stepping without any identification of the slow or the fast variables. In the special case of deterministic and stochastic mechanical systems, symplectic, multisymplectic, and quasi-symplectic multiscale integrators are easily obtained using this strategy. For highly oscillatory mechanical systems (with quasi-quadratic stiff potentials and possibly high-dimensional), a specialized symplectic method has been devised to provide improved efficiency and accuracy. This method is based on the introduction of two highly nontrivial matrix exponentiation algorithms, which are generic, efficient, and symplectic (if the exact exponential is symplectic). For multiscale systems with Dirac-distributed fast processes, a family of symplectic, linearly-implicit and stable integrators has been designed for coarse step simulations. An application is the fast and accurate integration of constrained dynamics. In addition, if one cares about statistical properties of an ensemble of trajectories, but not the numerical accuracy of a single trajectory, we suggest tuning friction and annealing temperature in a Langevin process to accelerate its convergence. Other works include variational integration of circuits, efficient simulation of a nonlinear wave, and finding optimal transition pathways in stochastic dynamical systems (with a demonstration of mass effects in molecular dynamics).

This study compares the newly developed stochastic agent-based model (ABM) and deterministic system within the context of COVID-19 transmission dynamics, using the Susceptible-Exposed-Infectious-Removed-Perception (SEIRP) compartments. The primary aim is to analyse the similarities and differences between these modelling approaches, providing insights into the emergent behaviours of the epidemiological systems. We also investigate how social phenomena like public perception affect the two epidemiological models’ outcomes, focusing on scenarios with varying awareness levels and proportions of severe cases. Our results show that while deterministic model outputs align well with the ABM for large populations, discrepancies emerge for small populations in the ABM due to the impacts of stochastic extinction and discreteness of individuals. In scenarios with high proportions of severe cases and for large population sizes, the deterministic model exhibits oscillatory behaviour. In this situation, the averaged ABM densities initially capture the fluctuation dynamics when a substantial number of realisations is used in simulation; however, this stochastic system exhibits diminishing fluctuations across different realisations, contributing to a consistent average akin to an endemic steady state over a longer period. Interestingly, as the number of realisations is reduced, the agreement between stochastic and deterministic systems in depicting recurrent outbreaks is evident (i.e., realisations-dependent dynamical behaviour). In the next case study, the joint effect of recovery, latency period, disease severity and public perception is explored, highlighting how different factors can combine to influence the systems’ outcomes. Notably, our case study finds that even when the deterministic model demonstrates the persistence of sustained oscillations, the ABM can depict an extinction state; this stochastic ABM observation is caused by the realisations in this epidemiological system fluctuating to a very low population density and being excluded over a long run. Overall, our findings suggest the importance of considering both deterministic and stochastic models in infectious disease modelling, highlighting the need for comprehensive analyses to guide evidence-based decision-making in public health and epidemiology.

Dynamic pricing depends on the understanding of uncertain demand. We ask the question whether a stochastic system is sufficient to model this uncertainty. We propose a novel paradigm based on statistical analysis of recurrence quantification measures. The paradigm fits nonlinear dynamics by simultaneously optimizing both the determinism and the trapping time in recurrence plots and identifies an optimal time delay embedding. We firstly apply the paradigm on well-known deterministic and stochastic systems including Duffing systems and multi-fractional Gaussian noise. We then apply the paradigm to optimize the sampling of empirical point process data from RideAustin, a company providing ride share service in the city of Austin, Texas, the USA, thus reconstructing a period-7 attractor. Results show that in deterministic systems, an optimal embedding exists under which recurrence plots exhibit robust diagonal or vertical lines. However, in stochastic systems, an optimal embedding often does not exist, evidenced by the inability to shrink the standard deviation of either the determinism or the trapping time. By means of surrogate testing, we also show that a Poisson process or a stochastic system with periodic trend is insufficient to model uncertainty contained in empirical data. By contrast, the period-7 attractor dominates and well models nonlinear dynamics of empirical data via irregularly switching of the slow and the fast dynamics. Findings highlight the importance of fitting and recreating nonlinear dynamics of data in modeling practical problems.

Deterministic models have played an important role in biology over the past few centuries, and continue to play. However, there is a need to introduce a random factor due to the nature of biological processes, which led to the emergence of stochastic models. To bridge the gap between deterministic and stochastic models, and in appreciation of the huge efforts made in deterministic models, which have played a fundamental role in understanding, predicting, and controlling the transmission dynamics of infectious diseases, this study aims to clarify a method for creating stochastic model for one of most vital and recent deterministic, that is Covid-19 model with two doses, which showed the effect of receiving both doses in containing the disease. To achieve this goal, the deterministic model was studied extensively before converting to corresponding stochastic. The deterministic and stochastic systems were solved numerically and represented using MATAB tools. Keywords: Covid-19 deterministic model, stochastic model, next generation matrix method (NGMM), The diffusion matrix G.

In this paper, we demonstrate that the exact controllability concept is inappropriate for stochastic systems by proving the nonfulfillment of the coercivity inequality. As a result, we explore the concept of C-controllability, which was introduced as a weaker alternative to exact controllability when the controllability problem was first extended to stochastic systems. We establish a sufficient condition for C-controllability in finite-dimensional semilinear stochastic systems driven by a Wiener process and illustrate the result with several examples.

We look at the behavior of biological oscillators, specifically analyzing a genetic circuit that has oscillatory behavior. Implementing the system in deterministic and stochastic models, we compare the two models using various tests and analyze the effect of stochastic noise on these oscillations. We first investigate the effect of volume and find that at small system volumes, stochastic events cause the system to exhibit more sporadic oscillations and a longer period of oscillation. Next, we develop classification systems for discerning the boundary in the parameter space where the model begins to show oscillations. This is more challenging in the stochastic model, due to persistent fluctuations. We compare different methods of analysis of our deterministic and stochastic models and visualize the conditions which lead to oscillation in each model. We find that the parameter range of oscillation is larger in the stochastic model than in the deterministic model. Finally, we find an increased rate of mRNA production can create greater noise in a system and amplify the difference between a stochastic and deterministic system.

Stochastic perturbations for a duopoly Stackelberg model

We consider a class of nonlinear stochastic systems driven by Wiener and Poisson processes. The Wiener process input enters into the equations additively to the dynamics while the Poisson process input enters into the equations multiplicatively to the control input. Examples of applied problems that may lead to system models of this kind are discussed. The optimal containment control problem is then formulated for these systems. It involves either maximizing the time of stay within an admissible set or a closely related performance measure. The optimal control and the optimal value function are characterized on the basis of Bellman's dynamic programming principle in the general case so that the optimal value function is a solution of a boundary value problem for a PDE. For a special case defined by more restrictive assumptions the method of successive approximations is used to show the existence of a solution to this boundary value problem and to set up an iterative solution procedure. An example is reported that illustrates the results.

Time evolution, ergodic properties, and invariant measures of some classical fields described by second-order differential equations are discussed. The classical system is not ergodic. A class of Markovian random perturbations is considered. It is shown that the stochastic system with a fixed stochastic perturbation is uniquely ergodic. We discuss in detail the Euler equation in two dimensions and the wave equation in one dimension. In our models an arbitrarily small stochastic perturbation selects one of the invariant measures for the deterministic system.

Stochastic Compound Dynamic Systems (CDS) are complex technical systems that are created through the use of precision mechanics in combination with modern telecommunications and computer technologies. Incertitude in these CDS shows up under the influence of external and internal stochastic perturbations. The constituent elements of CDS are combined into a single system because these elements perform a single complex mission. The information exchange is wireless, there is no mechanical connection between the elements of the CDS. The paper considers groups of unmanned aerial vehicles (UAVs), which are equipped with sensors or multisensors that are able to perform a mobile sensor network. The trajectories of individual elements of the mobile sensor network are trajectories formed under the influence of stochastic perturbations. This fact means that the nature of the mobile sensor network can be classified as a stochastic compound dynamic system and for the mathematical description and optimization of the movement of this system is adequate to use models and methods for optimizing stochastic CDS. The model of CDS motion is considered to be a branching trajectory or, as they say, a branched trajectory. A stochastic mathematical model of the motion of a mobile sensor network, which performs the combined mission of surveying an emergency zone, can be classified as a model of the motion of a stochastic compound dynamic system. This approach is an adequate for mathematical model creation to the mobile sensor network physical condition, for its operation in the zone of natural disaster by natural or anthropogenic origin. This paper is devoted to solving a theoretical problem related to the formulation and proof of the necessary conditions for stochastic CDS moving optimal control along a branched trajectory with an arbitrary branching scheme.

This article is mainly concerned with the fixed-time and predefined-time synchronization problem for a type of complex-valued BAM neural networks with stochastic perturbations and impulse effect. First, some previous fixed-time stability results on nonlinear impulsive systems in which stabilizing and destabilizing impulses were separately analyzed are extended to a general case in which the stabilizing and destabilizing impulses can be handled simultaneously. Additionally, using the same logic, a new predefined-time stability lemma for stochastic nonlinear systems with a general impulsive effect is obtained by using the inequality technique. Then, based on these novel results, two novel controllers are implemented to derive some simple fixed/predefined-time synchronization criteria for the considered complex-valued impulsive BAM neural networks with stochastic perturbations using the non-separation method. Finally, two numerical examples are given to demonstrate the feasibility of the obtained results.

Another proof for the equivalence between invariance of closed sets with respect to stochastic and deterministic systems

We discuss a stochastic SIQR epidemic system with vaccination, in this paper. This system does not have any equilibrium points compared with the corresponding deterministic SIQR model, but we explore the asymptotic behaviors of its solutions to the equilibria of the corresponding deterministic SIQR system, and the global existence of positive solution. By I to's formula and constructing proper Lyapunov functions, we prove that the solutions for stochastic SIQR system fluctuate randomly around equilibrium points of the corresponding deterministic SIQR system under some assumptions. At last, numerical simulations are used to verify our conclusions, and we reveal that this class of stochastic SIQR system with vaccination and isolation possesses realistic great significance.