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Zhao Dong

Publications and source records attributed to Zhao Dong.

At least 55 records · Page 3Linked to original sources
arXiv2020

Large deviation principles for first-order scalar conservation laws with stochastic forcing

In this paper, we established the Freidlin-Wentzell type large deviation principles for first-order scalar conservation laws perturbed by small multiplicative noise. Due to the lack of the viscous terms in the stochastic equations, the kinetic solution to the Cauchy problem for these first-order conservation laws is studied. Then, based on the well-posedness of the kinetic solutions, we show that the large deviations holds by utilising the weak convergence approach.

math.PR↗
arXiv2020

3D tamed Navier-Stokes equations driven by multiplicative Lévy noise: Existence, uniqueness and large deviations

In this paper, we show the existence and uniqueness of a strong solution to stochastic 3D tamed Navier-Stokes equations driven by multiplicative Levy noise with periodic boundary conditions. Then we establish the large deviation principles of the strong solution on the state space $\mathcal{D}([0,T];\mathbb{H}^1)$, where the weak convergence approach plays a key role.

math.PR↗
arXiv2018

Exponential Mixing for 3D Stochastic Primitive Equations

In this paper, we prove that weak solutions of 3D stochastic primitive equations have exponential mixing property if the noise is sufficiently smooth and non-degenerate. With the help of uniqueness of strong solution of 3D stochastic primitive equations, we obtain that all weak solutions which are limitations of Galerkin approximations share the same invariant measure. In particular, the invariant measure of strong solution is unique. The coupling method plays a key role.

math.PR↗
arXiv2018

Ergodicity for a class of semilinear stochastic partial differential equations

In this paper, we establish the existence and uniqueness of invariant measures for a class of semilinear stochastic partial differential equations driven by multiplicative noise on a bounded domain. The main results can be applied to SPDEs of various types such as the stochastic Burgers equation and the reaction-diffusion equations perturbed by space-time white noise.

math.PR↗
arXiv2018

Averaging principle for one dimensional stochastic Burgers equation

In this paper, we consider the averaging principle for one dimensional stochastic Burgers equation with slow and fast time-scales. Under some suitable conditions, we show that the slow component strongly converges to the solution of the corresponding averaged equation. Meanwhile, when there is no noise in the slow component equation, we also prove that the slow component weakly converges to the solution of the corresponding averaged equation with the order of convergence $1-r$, for any $0<r<1$.

math.PR↗
arXiv2018

On the small time asymptotics of 3D stochastic primitive equations

In this paper, we establish a small time large deviation principle for the strong solution of 3D stochastic primitive equations driven by multiplicative noise. Both the small noise and the small, but highly nonlinear, unbounded nonlinear terms should be taken into consideration.

math.PR↗
arXiv2017

Robust Dynamic Selection of Tested Modules in Software Testing for Maximizing Delivered Reliability

Software testing is aimed to improve the delivered reliability of the users. Delivered reliability is the reliability of using the software after it is delivered to the users. Usually the software consists of many modules. Thus, the delivered reliability is dependent on the operational profile which specifies how the users will use these modules as well as the defect number remaining in each module. Therefore, a good testing policy should take the operational profile into account and dynamically select tested modules according to the current state of the software during the testing process. This paper discusses how to dynamically select tested modules in order to maximize delivered reliability by formulating the selection problem as a dynamic programming problem. As the testing process is performed only once, risk must be considered during the testing process, which is described by the tester's utility function in this paper. Besides, since usually the tester has no accurate estimate of the operational profile, by employing robust optimization technique, we analysis the selection problem in the worst case, given the uncertainty set of operational profile. By numerical examples, we show the necessity of maximizing delivered reliability directly and using robust optimization technique when the tester has no clear idea of the operational profile. Moreover, it is shown that the risk averse behavior of the tester has a major influence on the delivered reliability.

cs.SE↗
arXiv2016

On Limiting Behavior of Stationary Measures for Stochastic Evolution Systems with Small Noise Intensity

The limiting behavior of stochastic evolution processes with small noise intensity $ε$ is investigated in distribution-based approach. Let $μ^ε$ be stationary measure for stochastic process $X^ε$ with small $ε$ and $X^{0}$ be a semiflow on a Polish space. Assume that $\{μ^ε: 0<ε\leqε_0\}$ is tight. Then all their limits in weak sense are $X^0-$invariant and their supports are contained in Birkhoff center of $X^0$. Applications are made to various stochastic evolution systems, including stochastic ordinary differential equations, stochastic partial differential equations, stochastic functional differential equations driven by Brownian motion or Lévy process.

math.PR↗
arXiv2016

Markov Selection and $W$-strong Feller for 3D Stochastic Primitive Equations

This paper studies some analytical properties of weak solutions of 3D stochastic primitive equations with periodic boundary conditions. The martingale problem associated to this model is shown to have a family of solutions satisfying the Markov property, which is achieved by means of an abstract selection principle. The Markov property is crucial to extend the regularity of the transition semigroup from small times to arbitrary times. Thus, under a regular additive noise, every Markov solution is shown to have a property of continuous dependence on initial conditions, which follows from employing the weak-strong uniqueness principle and the Bismut-Elworthy-Li formula.

math.PR↗
arXiv2016

Long-time behavior of 3D Stochastic Planetary Geostrophic Viscous Model

This paper reports the 3D planetary geostrophic viscous model has the exponential ergodicity and global attractor if this model is driven by an additive random noise, which results in the support of the integration of invariant measure for the dynamic is exactly a minimal global attractor. It's worth mentioning that this global attractor has finite Hausdorff dimension.

math.PR↗
arXiv2016

The Decomposition Formula and Stationary Measures for Stochastic Lotka-Volterra Systems with Applications to Turbulent Convection

Motivated by the work of Busse et al. [6] on turbulent convection in a rotating layer, we exploit the long-run behavior for stochastic Lotka-Volterra (LV) systems both in pull-back trajectory and in stationary measure. It is proved stochastic decomposition formula describing the relation between solutions of stochastic and deterministic LV systems and stochastic Logistic equation. By virtue of this formula, it is verified that every pull-back omega limit set is an omega limit set for deterministic LV systems multiplied by the random equilibrium of the stochastic Logistic equation. This formula is used to derive the existence of a stationary measure, its support and ergodicity. We prove the tightness for the set of stationary measures and the invariance for their weak limits as the noise intensity vanishes, whose supports are contained in the Birkhoff center. The developed theory is successfully utilized to completely classify three dimensional competitive stochastic LV systems into $37$ classes. Time average probability measures weakly converge to an ergodic stationary measure on the attracting domain of an omega limit set in all classes except class 27 c). Among them there are two classes possessing a continuum of random closed orbits and ergodic stationary measures supported in cone surfaces, which weakly converge to the Haar measures of periodic orbits as the noise intensity vanishes. In the exceptional class, almost every pull-back trajectory cyclically oscillates around the boundary of the stochastic carrying simplex characterized by three unstable stationary solutions. The limit for the time average probability measures is neither unique nor ergodic. These are subject to turbulent characteristics.

math.DS↗
arXiv2014

On some smoothening effects of the transition semigroup of a Lévy process

Let $(P_t)$ be the transition semigroup of a Lévy process $L$ taking values in a Hilbert space $H$. Let $ν$ be the Lévy measure of $L$. It is shown that for any bounded and measurable function $f$, $$ \int_H\left\vert P_tf(x+y)-P_tf(x)\right\vert ^2 ν(\dif y)\le \frac 1 t P_tf^2(x) \qquad \text{for all $t>0$, $x\in H$.} $$ As $ν$ can be infinite this formula establishes some smoothening effect of the semigroup $(P_t)$. In the paper some applications of the formula will be presented as well.

math.PR↗
arXiv2014

Strong Feller properties for degenerate SDEs with jumps

Under full Hörmander's conditions, we prove the strong Feller property of the semigroup determined by an SDE driven by additive subordinate Brownian motion, where the drift is allowed to be arbitrarily growth. For this, we extend a criterion due to Malicet-Poly \cite{Ma-Po} and Bally-Caramellino \cite{Ba-Ca} about the convergence of the laws of Wiener functionals in total variations. Moreover, the example of a chain of coupled oscillators is verified.

math.PR↗
arXiv2014

A Note on Distribution Free Symmetrization Inequalities

Let $X, Y$ be two independent identically distributed (i.i.d.) random variables taking values from a separable Banach space $(\mathcal{X}, \|\cdot\|)$. Given two measurable subsets $F, K\subseteq\cal{X}$, we established distribution free comparison inequalities between $\mathbb{P}(X\pm Y \in F)$ and $\mathbb{P}(X-Y\in K)$. These estimates are optimal for real random variables as well as when $\mathcal{X}=\mathbb{R}^d$ is equipped with the $\|\cdot\|_\infty$ norm. Our approach for both problems extends techniques developed by Schultze and Weizsächer (2007).

math.PR↗
arXiv2012

Exponential Ergodicity of stochastic Burgers equations driven by $α$-stable processes

In this work, we prove the strong Feller property and the exponential ergodicity of stochastic Burgers equations driven by $α/2$-subordinated cylindrical Brownian motions with $α\in(1,2)$. To prove the results, we truncate the nonlinearity and use the derivative formula for SDEs driven by $α$-stable noises established in Zhang (arXiv:1204.2630v2).

math.PR↗