Nonlinear Stochastic Differential Equations Research Articles

Strong convergence of a class of adaptive numerical methods for SDEs with jumps

We develop adaptive time-stepping strategies for Itô-type stochastic differential equations (SDEs) with jump perturbations. Our approach builds on adaptive strategies for SDEs.Adaptive methods can ensure strong convergence of nonlinear SDEs with drift and diffusion coefficients that violate global Lipschitz bounds by adjusting the stepsize dynamically on each trajectory to prevent spurious growth that can lead to loss of convergence if it occurs with sufficiently high probability.In this article, we demonstrate the use of a jump-adapted mesh that incorporates jump times into the adaptive time-stepping strategy. We prove that any adaptive scheme satisfying a particular mean-square consistency bound for a nonlinear SDE in the non-jump case may be extended to a strongly convergent scheme in the Poisson jump case, where the jump and diffusion perturbations are mutually independent, and the jump coefficient satisfies a global Lipschitz condition.

Computational Methods for Stationary and Nonstationary Fokker–Planck–Kolmogorov Equations with Random Coefficients

In this paper, methodological approaches for distribution functions of nonlinear stochastic differential equations (SDEs) with uncertain parameters are elaborated. The stationary and nonstationary Fokker–Planck–Kolmogorov (FPK) equations associated with the considered random equations are investigated. A semi-analytical approach based on the extended exponential closure handling the parameters uncertainty effects is presented. A new numerical method coupling the polynomial chaos with the differential quadrature method (DQM) is elaborated for the stationary case. A compact matrix formulation is established allowing considering a large number of generalized polynomial chaos. For nonstationary random FPK equations, the polynomial chaos is coupled with the quadrature discretization. A symmetric discretization of the main Fokker–Planck operator using a tensor product of the Hilbert state space and the Hilbert probabilistic space has resulted. The obtained eigenvalues and random eigenfunctions are used for a spectral decomposition. The time solution is obtained by the spectral expansion as well as by the random Euler stochastic method. The accuracy, effectiveness and advantages of the developed procedure in analyzing the stationary and nonstationary probabilistic solutions of nonlinear stochastic dynamical systems with uncertain parameters excited by a Gaussian white noise are demonstrated.

Risk-sensitive benchmarked portfolio optimization under non-linear market dynamics

We discuss a continuous-time portfolio optimization problem to beat a stochastic benchmark. The model considers non-linear stochastic differential equations (SDEs) to model the dynamics of assets and economic factors. Unlike existing literature on risk-sensitive criteria, the proposed framework allows the model to capture the non-linearity in assets and factors dynamics. This article contributes to the two essential aspects of the problem; first, the existence and uniqueness of optimal investment strategy, which we prove using stochastic control theory and shows optimal strategies remain unchanged for the finite and infinite time horizon problems. Second, we use forecasting to compare investment performance under different economic factors. We analyze returns for the proposed model for three years in two important financial markets; S&P 100 and Dow 30. Risk versus return analysis indicates the importance of choosing relevant economic factors and their model for better portfolio performance. The results suggest an excellent bet to select the non-linear model over the linear one.

Ultimate boundedness and stability of highly nonlinear neutral stochastic delay differential equations with semi-Markovian switching signals

The ultimate boundedness and stability of highly nonlinear neutral stochastic delay differential equations (NSDDEs) are investigated in this paper. Different from many previous works, the highly nonlinear NSDDEs with semi-Markov switching signals are considered for the first time in this paper. Meanwhile, the time delay function in this paper is only required to meet much more relaxed restrictions, which can invalidate plentiful methods with requirements on its derivatives for the stability of NSDDEs. To overcome this difficulty, several novel techniques to tackle NSDDEs with such a delay are developed. Furthermore, a crucial property on the ergodic semi-Markov process is established, which plays a key role in the proof on the existence and boundedness of global solution. The generalized Khasminskii-type theorems are established for the existence and uniqueness of global solution by applying new methods and this property, and the criteria for boundedness and stability are also provided. In particular, feasible measures are provided to deal with the neutral term in our stability criteria. Finally, the effectiveness is verified by a numerical example.

Stochastic nonlinear model for nanoparticles and viruses escape from endosomes

AbstractA stochastic nonlinear model for Rab5 and Rab7 proteins describing the transformation of early endosomes into late endosomes was formulated. This model consists of two stochastic nonlinear differential equations for Rab5 and Rab7 protein levels on the endosome surface. The primary goal of this paper is to understand the impact of multiplicative noise on the nonlinear dynamics of Rab5 and Rab7. The main idea is to introduce the stochastic variable T, which defines the random time when the conversion from Rab5 to Rab7 occurs. It follows from the dynamics of pH level that T can also be considered as the escape time of pH-sensitive nanoparticles and viruses from endosomes. The probability density function for T was obtained numerically. It was shown that the average conversion time T is shifted to the right when compared to the deterministic one, potentially influencing the pH distribution function and, consequently, the average escape time of viruses and nanoparticles.

Novel criteria for exponential stability in mean square of stochastic delay differential equations with Markovian switching

This paper addresses the exponential stability in mean square of nonlinear stochastic delay differential equations with Markovian switching. Using a novel approach, we present new explicit criteria for exponential stability in mean square. A discussion and two examples are given to illustrate the effectiveness of our criteria.

Deep learning-based estimation of time-dependent parameters in Markov models with application to nonlinear regression and SDEs

We present a novel deep-learning method for estimating time-dependent parameters in Markov processes through discrete sampling. Departing from conventional machine learning, our approach reframes parameter approximation as an optimization problem using the maximum likelihood approach. Experimental validation focuses on parameter estimation in multivariate regression and stochastic differential equations (SDEs). Theoretical results show that the real solution is close to SDE with parameters approximated using our neural network derived under specific conditions. Our work contributes to SDE-based model parameter estimation, offering a versatile tool for diverse fields.

Stochastic electromechanical bidomain model *

We analyse a system of nonlinear stochastic partial differential equations (SPDEs) of mixed elliptic-parabolic type that models the propagation of electric signals and their effect on the deformation of cardiac tissue. The system governs the dynamics of ionic quantities, intra and extra-cellular potentials, and linearised elasticity equations. We introduce a framework called the active strain decomposition, which factors the material gradient of deformation into an active (electrophysiology-dependent) part and an elastic (passive) part, to capture the coupling between muscle contraction, biochemical reactions, and electric activity. Under the assumption of linearised elastic behaviour and a truncation of the nonlinear diffusivities, we propose a stochastic electromechanical bidomain model, and establish the existence of weak solutions for this model. To prove existence through the convergence of approximate solutions, we employ a stochastic compactness method in tandem with an auxiliary non-degenerate system and the Faedo–Galerkin method. We utilise a stochastic adaptation of de Rham’s theorem to deduce the weak convergence of the pressure approximations.

Existence and Hyers–Ulam Stability of Stochastic Delay Systems Governed by the Rosenblatt Process

Under the effect of the Rosenblatt process, time-delay systems of nonlinear stochastic delay differential equations are considered. Utilizing the delayed matrix functions and exact solutions for these systems, the existence and Hyers–Ulam stability results are derived. First, depending on the fixed point theory, the existence and uniqueness of solutions are proven. Next, sufficient criteria for the Hyers–Ulam stability are established. Ultimately, to illustrate the importance of the results, an example is provided.

A Collocation Method for Nonlinear Stochastic Differential Equations Driven by Fractional Brownian Motion and its Application to Mathematical Finance

A Collocation Method for Nonlinear Stochastic Differential Equations Driven by Fractional Brownian Motion and its Application to Mathematical Finance

Stabilization and destabilisation of non-autonomous stochastic nonlinear delay differential equations

This paper focuses on a class of non-autonomous stochastic nonlinear delay differential equations, where the time delay functions are no longer required to be bounded or differentiable. The key aim is to study the stabilisation and destabilisation of the non-autonomous nonlinear stochastic delay differential equations by using the function ln ⁡ | x ( t ) | 2 , the improved LaSalle-type theorem, and non-negative semimartingale convergence theorem. In comparison with recent works on stabilisation for stochastic time-varying delay nonlinear systems (see, e.g. [Dong, H., & Mao, X. (2022). Advances in stabilization of highly nonlinear hybrid delay systems. Automatica, 136, 110086. https://doi.org/10.1016/j.automatica.2021.110086; Hu, J., Mao,W., & Mao, X. (2023). Advances in nonlinear hybrid stochastic differential delay equations: Existence, boundness and stability. Automatica, 147, 110682. https://doi.org/10.1016/j.automatica.2022.110682; Xu, H., & Mao, X. (2023). Improved delay-dependent stability of superlinear hybrid stochastic systems with general time-varying delays. Nonlinear Analysis: Hybrid Systems, 50, 101413. https://doi.org/10.1016/j.nahs.2023.101413]), the proposed results exhibit significant improvements and can be applied to a broader class of non-autonomous equations.

Stochastic evolution equations with Wick-analytic nonlinearities

We study nonlinear stochastic partial differential equations with Wick-analytic type nonlinearities set in the framework of white noise analysis. These equations include the stochastic Fisher–KPP equations, stochastic Allen–Cahn, stochastic Newell–Whitehead–Segel, and stochastic Fujita–Gelfand equations. By implementing the theory of C 0 − semigroups and evolution systems into the chaos expansion theory in infinite dimensional spaces, we prove the existence and uniqueness of solutions for this class of stochastic partial differential equations.

Simulating variable‐order fractional Brownian motion and solving nonlinear stochastic differential equations

Stochastic differential equations (SDEs) are very useful in modeling many problems in biology, economic data, turbulence, and medicine. Fractional Brownian motion (fBm) and variable‐order fractional Brownian motion (vofBm) are suitable alternatives to standard Brownian motion (sBm) for describing and modeling many phenomena, since the increments of these processes are dependent of the past and for these increments have the property of long‐term dependence. Classical mathematical techniques such as Ito's calculus do not work for stochastic computations on fBm and vofBm due to they are not semi‐Martingale for . Therefore, solving these equations is much more difficult than solving SDEs with sBm. On the other hand, these equations do not have an analytical solution, so we have to use numerical methods to find their solution. In this paper, a computational approach based on hybrid of block‐pulse and parabolic functions (HBPFs) has been introduced for simulating vofBm and solving a modern class of SDEs. The mechanism of this approach is based on stochastic and fractional integration operational matrices, which transform the intended problem to a nonlinear system of algebraic equations. Thus, the complexity of solving the mentioned problem is reduced significantly. Also, convergence analysis of the expressed method has been theoretically examined. Finally, the accuracy and efficiency of the proposed algorithm have been experimentally investigated through some test problems and comparison of obtained results with results of previous papers. High accurate numerical results are obtained by using a small number of basic functions. Therefore, this method deals with smaller matrices and vectors, which is one of the most important advantage of our suggested method. Also, presenting an applicable procedure to construct vofBm is another innovation of this work. To gain this aim, at first, discretized sBm is generated via fundamental features of this process, and afterward, block‐pulse functions (BPFs) and HBPFs are utilized for simulating discretized vofBm. Finally, spline interpolation method has been employed to provide a continuous path of vofBm.

Local Lipschitz Continuity in the Initial Value and Strong Completeness for Nonlinear Stochastic Differential Equations

Recently, Hairer et al. (2015) showed that there exist stochastic differential equations (SDEs) with infinitely often differentiable and globally bounded coefficient functions whose solutions fail to be locally Lipschitz continuous in the strong L p L^p -sense with respect to the initial value for every p ∈ ( 0 , ∞ ] p\in (0,\infty ] . In this article we provide conditions on the coefficient functions of the SDE and on p ∈ ( 0 , ∞ ] p \in (0,\infty ] that are sufficient for local Lipschitz continuity in the strong L p L^p -sense with respect to the initial value and we establish explicit estimates for the local Lipschitz continuity constants. In particular, we prove local Lipschitz continuity in the initial value for several nonlinear stochastic ordinary and stochastic partial differential equations in the literature such as the stochastic van der Pol oscillator, Brownian dynamics, the Cox-Ingersoll-Ross processes and the Cahn-Hilliard-Cook equation. As an application of our estimates, we obtain strong completeness for several nonlinear SDEs.

Boundedness and stability of nonlinear hybrid neutral stochastic delay differential equation with Lévy jumps under different structures

This paper investigates the boundedness and stability of a class of nonlinear hybrid neutral stochastic differential delay systems with Lévy jumps and different structures. The coefficients in this system satisfy the local Lipschitz condition and a suitable Khasminskii-type condition, and the state space of the system is separated into two subsets, the existence uniqueness, asymptotic boundedness, and exponential stability of the system are obtained by designing a new Lyapunov function and applying the M-matrix technique as well as dealing with the non-differentiable delay function. Different with the existing work, we not only consider the neutral term, but also the case of the delay function being bounded and non-differentiable. At last, numerical examples are performed to manifest the obtained results.

Parameter estimation in nonlinear multivariate stochastic differential equations based on splitting schemes

Parameter estimation in nonlinear multivariate stochastic differential equations based on splitting schemes

Stabilization by discrete‐time feedback control for highly nonlinear hybrid neutral stochastic functional differential equations with infinite delay

The problem of stabilizing an unstable highly nonlinear hybrid neutral stochastic functional differential equation with infinite delay (HNSFDEwID) is addressed in this article. The objective of this study is to propose a discrete‐time feedback control (DTFC) approach for stabilizing the original system. Sufficient conditions are derived for the existence and uniqueness of the system's solution, as well as its boundedness, prerequisites for studying the system's dynamic behavior. Four distinct criteria for stabilizing the controlled system are provided, including stabilization, asymptotic stabilization, exponential stabilization, and almost surely exponentially stabilization. Additionally, the upper limit of the time interval for discrete observations is calculated based on these criteria. Finally, the proposed theory is validated through an illustrative example.

Adaptively tracking hypersonic gliding vehicles

The motion model plays a key role in tracking hypersonic gliding vehicles (HGVs). The process noise covariance matrix is used to scale the errors of the motion model, and its design has significant influence on the tracking accuracy. In this paper, a dynamics model with an adaptive process noise covariance matrix is proposed for tracking HGVs. The dynamics model is described by a nonlinear stochastic differential equation (SDE), and its corresponding process noise covariance matrix is calculated by adopting the modified Gauss-Legendre scheme. Furthermore, an adaptive adjustment method for process noise covariances of the augmented states is designed by analyzing the variation characteristics of the model errors. Specifically, the process noise covariances are adjusted by an adaptive factor: the square of the ratio of the estimated dynamic pressure to a prior dynamic pressure. Simulation results show that the proposed model can provide higher tracking accuracy than the dynamics model with fixed process noise covariances.

Stability Analysis for Nonlinear Neutral Stochastic Functional Differential Equations

Stability Analysis for Nonlinear Neutral Stochastic Functional Differential Equations

Precision in disease dynamics: Finite difference solutions for stochastic epidemics with treatment cure and partial immunity

The complex and ever-changing characteristics of epidemic modelling, particularly when considering random elements, provide a substantial obstacle in creating precise and practical numerical methods for solving differential equations. This study contributes to this effort by introducing an innovative finite difference method for linear and non-linear stochastic and deterministic differential equations. This scheme expands explicitly upon the Euler Maruyama method, improving its precision for the deterministic aspect while ensuring coherence in dealing with stochastic terms. This contribution provides a numerical scheme that can be used to find solutions to linear and non-linear stochastic and deterministic differential equations. The scheme can be considered as the extension of the Euler Maruyama scheme for solving stochastic differential equations. The Euler Maruyama scheme offers a first-order accuracy of the deterministic model. Still, this scheme provides second-order accuracy for the deterministic part, whereas the integration of stochastic terms is the same in both schemes. The scheme is employed in a stochastic diffusive epidemic model with the effect of treatment, cure, and partial immunity. The comparison of the proposed scheme with the existing nonstandard finite difference method is made, and it is shown that the proposed scheme performs better than the nonstandard finite difference method in accuracy for the deterministic differential equations. It is also demonstrated that susceptible people rise whereas infected and recovered people decline by enhancing treatment cure rate. How does the cure rate of the treatment influence the number of the three populations, i.e., S(t), I(t), and R(t)? The results from the numerical simulation have provided useful insights into the dynamics of the epidemic model under various settings. This is particularly useful for influencing any public health plan and intervention. Thus, this work contributes numerical approaches and is an essential tool for epidemiological studies.