arXiv subjects

Jinqiao Duan

Publications and source records attributed to Jinqiao Duan.

At least 199 records · Page 11Linked to original sources

arXiv2012

Asymmetric non-Gaussian effects in a tumor growth model with immunization

The dynamical evolution of a tumor growth model, under immune surveillance and subject to asymmetric non-Gaussian $α$-stableLévy noise, is explored. The lifetime of a tumor staying in the range between the tumor-free state and the stable tumor state, and the likelihood of noise-inducing tumor extinction, are characterized by the mean exit time (also called mean residence time) and the escape probability, respectively. For various initial densities of tumor cells, the mean exit time and the escape probability are computed with different noise parameters. It is observed that unlike the Gaussian noise or symmetric non-Gaussian noise, the asymmetric non-Gaussian noise plays a constructive role in the tumor evolution in this simple model. By adjusting the noise parameters, the mean exit time can be shortened and the escape probability can be increased, simultaneously. This suggests that a tumor may be mitigated with higher probability in a shorter time, under certain external environmental stimuli.

math.DS↗

arXiv2012

Center manifolds for stochastic evolution equations

Stochastic invariant manifolds are crucial in modelling the dynamical behavior of dynamical systems under uncertainty. Under the assumption of exponential trichotomy, existence and smoothness of center manifolds for a class of stochastic evolution equations with linearly multiplicative noise are proved. The exponential attraction and approximation to center manifolds are also discussed.

math.DS↗

arXiv2012

Topological equivalence for discontinuous random dynamical systems and applications

After defining non-Gaussian Lévy processes for two-sided time, stochastic differential equations with such Lévy processes are considered. Solution paths for these stochastic differential equations have countable jump discontinuities in time. Topological equivalence (or conjugacy) for such an Itô stochastic differential equation and its transformed random differential equation is established. Consequently, a stochastic Hartman-Grobman theorem is proved for the linearization of the Itô stochastic differential equation. Furthermore, for Marcus stochastic differential equations,this topological equivalence is used to prove existence of global random attractors.

math.PR↗

arXiv2012

Approximating dynamics of a singularly perturbed stochastic wave equation with a random dynamical boundary condition

This work is concerned with a singularly perturbed stochastic nonlinear wave equation with a random dynamical boundary condition. A splitting skill is used to derive the approximating equation of the system in the sense of probability distribution, when the singular perturbation parameter is sufficiently small. The approximating equation is a stochastic parabolic equation when the power exponent of singular perturbation parameter is in $[1/2, 1)$, but a deterministic hyperbolic (wave) equation when the power exponent is in $(1, +\infty)$.

math.AP↗

arXiv2012

Non-Gaussian dynamics of a tumor growth system with immunization

This paper is devoted to exploring the effects of non-Gaussian fluctuations on dynamical evolution of a tumor growth model with immunization, subject to non-Gaussian α-stable type Lévy noise. The corresponding deterministic model has two meaningful states which represent the state of tumor extinction and the state of stable tumor, respectively. To characterize the lifetime for different initial densities of tumor cells staying in the domain between these two states and the likelihood of crossing this domain, the mean exit time and the escape probability are quantified by numerically solving differential integral equations with appropriate exterior boundary conditions. The relationships between the dynamical properties and the noise parameters are examined. It is found that in the different stages of tumor, the noise parameters have different influence on the lifetime and the likelihood inducing tumor extinction. These results are relevant for determining efficient therapeutic regimes to induce the extinction of tumor cells.

math.DS↗

arXiv2012

Ensemble Averaging for Dynamical Systems under Fast Oscillating Random Boundary Conditions

This paper is devoted to provide a theoretical underpinning for ensemble forecasting with rapid fluctuations in body forcing and in boundary conditions. Ensemble averaging principles are proved under suitable `mixing' conditions on random boundary conditions and on random body forcing. The ensemble averaged model is a nonlinear stochastic partial differential equation, with the deviation process (i.e., the approximation error process) quantified as the solution of a linear stochastic partial differential equation.

math.PR↗

arXiv2012

Lévy Noise-Induced Stochastic Resonance in a Bistable System

Stochastic resonance phenomenon induced by non-Gaussian Lévy noise in a second-order bistable system is investigated. The signal-noise-ratio for different parameters is computed by an efficient numerical scheme. The influences of the noise intensity, stability index of Lévy noise and amplitude of external signal on the occurrence of stochastic resonance phenomenon are characterized. This implies that a high amplitude of signal not only enhances the output power spectrum of system but also promotes stochastic resonance, and a proper adjustment of Lévy noise intensity in a certain range enlarges the peak value of output power spectrum which is significant for stochastic resonance. Moreover, with the optimal damping parameter, lowering the stability index leads to larger fluctuations of Lévy noise, and further reduces the chance of the stochastic resonance.

cond-mat.stat-mech↗

arXiv2012

Simulating Stochastic Inertial Manifolds by a Backward-Forward Approach

A numerical approach for the approximation of inertial manifolds of stochastic evolutionary equations with multiplicative noise is presented and illustrated. After splitting the stochastic evolutionary equations into a backward and a forward part, a numerical scheme is devised for solving this backward-forward stochastic system, and an ensemble of graphs representing the inertial manifold is consequently obtained. This numerical approach is tested in two illustrative examples: one is for a system of stochastic ordinary differential equations and the other is for a stochastic partial differential equation.

math.DS↗

arXiv2012

Modeling nonlinear random vibration: Implication of the energy conservation law

Nonlinear random vibration under excitations of both Gaussian and Poisson white noises is considered. The model is based on stochastic differential equations, and the corresponding stochastic integrals are defined in such a way that the energy conservation law is satisfied. It is shown that Stratonovich integral and Di Paola-Falsone integral should be used for excitations of Gaussian and Poisson white noises, respectively, in order for the model to satisfy the underlining physical laws (e.g., energy conservation). Numerical examples are presented to illustrate the theoretical results.

math.DS↗

arXiv2012

Effective dynamics of stochastic wave equation with a random dynamical boundary condition

This work is devoted to the effective macroscopic dynamics of a weakly damped stochastic nonlinear wave equation with a random dynamical boundary condition. The white noises are taken into account not only in the model equation defined on a domain perforated with small holes, but also in the dynamical boundary condition on the boundaries of the small holes. An effective homogenized, macroscopic model is derived in the sense of probability distribution, which is a new stochastic wave equation on a unified domain, without small holes, with a usual static boundary condition.

math.AP↗

arXiv2012

Escape Probability for Stochastic Dynamical Systems with Jumps

The escape probability is a deterministic concept that quantifies some aspects of stochastic dynamics. This issue has been investigated previously for dynamical systems driven by Gaussian Brownian motions. The present work considers escape probabilities for dynamical systems driven by non-Gaussian Lévy motions, especially symmetric $α$-stable Lévy motions. The escape probabilities are characterized as solutions of the Balayage-Dirichlet problems of certain partial differential-integral equations. Differences between escape probabilities for dynamical systems driven by Gaussian and non-Gaussian noises are highlighted. In certain special cases, analytic results for escape probabilities are given.

math.DS↗

arXiv2012

Quantifying Model Uncertainties in the Space of Probability Measures

Due to lack of scientific understanding, some mechanisms may be missing in mathematical modeling of complex phenomena in science and engineering. These mathematical models thus contain some uncertainties such as uncertain parameters. One method to estimate these parameters is based on pathwise observations, i.e., quantifying model uncertainty in the space of sample paths for system evolution. Another method is devised here to estimate uncertain parameters, or unknown system functions, based on experimental observations of probability distributions for system evolution. This is called the quantification of model uncertainties in the space of probability measures. A few examples are presented to demonstrate this method, analytically or numerically.

math.PR↗

arXiv2012

An alternative expression of Di Paola and Falson's formula for stochastic dynamics

Di Paola and Falsone's formula is widely used in studying stochastic dynamics of nonlinear systems under Poisson white noise. In this short communication, an alternative expression is presented. Compared to Di Paola and Falsone's original expression, the alternative one is applicable under more general condition, and shows significantly improved performance in numerical implementation. The alternative expression turns out to be a special case of the Marcus integrals.

math.DS↗

arXiv2012

Fokker-Planck equations for nonlinear dynamical systems driven by non-Gaussian Levy processes

The Fokker-Planck equations describe time evolution of probability densities of stochastic dynamical systems and are thus widely used to quantify random phenomena such as uncertainty propagation. For dynamical systems driven by non-Gaussian Lévy processes, however, it is difficult to obtain explicit forms of Fokker-Planck equations because the adjoint operators of the associated infinitesimal generators usually do not have exact formulation. In the present paper, Fokker- Planck equations are derived in terms of infinite series for nonlinear stochastic differential equations with non-Gaussian Lévy processes. A few examples are presented to illustrate the method.

math.DS↗

arXiv2012

Mean exit time and escape probability for dynamical systems driven by Levy noise

The mean first exit time and escape probability are utilized to quantify dynamical behaviors of stochastic differential equations with non-Gaussian alpha-stable type Levy motions. Both deterministic quantities are characterized by differential-integral equations(i.e.,differential equations with non local terms) but with different exterior conditions. The non-Gaussianity of noises manifests as nonlocality at the level of mean exit time and escape probability. An objective of this paper is to make mean exit time and escape probability as efficient computational tools, to the applied probability community, for quantifying stochastic dynamics. An accurate numerical scheme is developed and validated for computing the mean exit time and escape probability. Asymptotic solution for the mean exit time is given when the pure jump measure in the Levy motion is small. From both the analytical and numerical results, it is observed that the mean exit time depends strongly on the domain size and the value of alpha in the alpha-stable Levy jump measure. The mean exit time can measure which of the two competing factors in alpha-stable Levy motion, i.e. the jump frequency or the jump size, is dominant in helping a process exit a bounded domain. The escape probability is shown to vary with the underlying vector field(i.e.,drift). The mean exit time and escape probability could become discontinuous at the boundary of the domain, when the process is subject to certain deterministic potential and the value of alpha is in (0,1).

math.NA↗

arXiv2012

Elementary bifurcations for a simple dynamical system under non-Gaussian Levy noises

Nonlinear dynamical systems are sometimes under the influence of random fluctuations. It is desirable to examine possible bifurcations for stochastic dynamical systems when a parameter varies. A computational analysis is conducted to investigate bifurcations of a simple dynamical system under non-Gaussian α-stable Levy motions, by examining the changes in stationary probability density functions for the solution orbits of this stochastic system. The stationary probability density functions are obtained by numerically solving a non local Fokker-Planck equation. This allows numerically investigating phenomenological bifurcation, or P-bifurcation, for stochastic differential equations with non-Gaussian Levy noises.

math.DS↗

arXiv2011

Mean Exit Time and Escape Probability for a Tumor Growth System under Non-Gaussian Noise

Effects of non-Gaussian $α-$stable Lévy noise on the Gompertz tumor growth model are quantified by considering the mean exit time and escape probability of the cancer cell density from inside a safe or benign domain. The mean exit time and escape probability problems are formulated in a differential-integral equation with a fractional Laplacian operator. Numerical simulations are conducted to evaluate how the mean exit time and escape probability vary or bifurcates when $α$ changes. Some bifurcation phenomena are observed and their impacts are discussed.

math.DS↗

arXiv2011

Approximation of invariant foliations for stochastic dynamical systems

Invariant foliations are geometric structures for describing and understanding the qualitative behaviors of nonlinear dynamical systems. For stochastic dynamical systems, however, these geometric structures themselves are complicated random sets. Thus it is desirable to have some techniques to approximate random invariant foliations. In this paper, invariant foliations are approximated for dynamical systems with small noisy perturbations, via asymptotic analysis. Namely, random invariant foliations are represented as a perturbation of the deterministic invariant foliations, with deviation errors estimated.

math.DS↗