SearcharxivSearch

arXiv subjects

Ilya Pavlyukevich

Publications and source records attributed to Ilya Pavlyukevich.

At least 19 recordsLinked to original sources

Diffusion limits of cyclic finite-velocity random motions along vector fields

We investigate diffusion approximations for a class of multivariate inhomogeneous finite-velocity random motions motivated by models of active particles. A particle alternates cyclically among prescribed velocity fields $V_1,\dots,V_p$ with random run times and evolves according to either a \emph{flight} dynamics, consisting of piecewise-linear motion, or a \emph{gliding} dynamics, in which the particle follows the corresponding velocity flow. Under a Kac-type scaling that couples vanishing run times with diverging particle speed, we prove weak convergence of both processes to multidimensional diffusions. It turns out that the order of successive cyclic motions affects the diffusion limit. In addition to the second-order term generated by fluctuations of the random run times, the limiting drift contains directional derivatives $DV_i[V_j]$ of the underlying vector fields. In Stratonovich form, part of this drift is expressed through their Lie brackets $[V_i,V_j]$. Thus, the generic non-commutativity of the microscopic motions survives the diffusive scaling and generates a macroscopic drift which depends on the cyclic order. The limiting drift also distinguishes the flight and gliding dynamics, despite their being driven by the same vector fields and run times. Several examples, including run-and-reverse motion and cyclic dynamics generated by multiple vector fields, illustrate how the cyclic switching protocol and the geometry of the underlying velocity fields shape the limiting diffusions.

math.PR

A Tail-Respecting Splitting Numerical Scheme for Lévy-Driven SDEs With Superlinear Drifts

We present an explicit numerical approximation scheme, denoted by $\{X^n\}$, for the effective simulation of solutions $X$ to a multivariate stochastic differential equation (SDE) with a superlinearly growing $κ$-dissipative drift, where $κ>1$, driven by a multiplicative heavy-tailed Lévy process that has a finite $p$-th moment, with $p>0$. We show that the strong $L^{p_X}$-convergence $\sup_{t\in[0,T]}\mathbf E \|X^n_t-X_t\|^{p_X}=\mathcal O (h_n^γ)$ holds for any $p_X\in (0,p+κ-1)$, which is exactly the range where the $p_X$-moment of the solution is known to be finite. Additionally, for any $p_X\in (0,p)$ we establish strong uniform convergence: $\mathbf E\sup_{t\in[0,T]} \|X^n_t-X_t\|^{p_X}=\mathcal{O} ( h_n^δ )$. In both cases we determine the convergence rates $γ$ and $δ$. In the special case of SDEs driven solely by a Brownian motion, our numerical scheme preserves super-exponential moments of the solution. The scheme $\{X^n\}$ is realized as a combination of a well-known Euler method with a Lie-Trotter type splitting technique.

math.PR

Limit theorems for sticky SDEs with local times and applications to stochastic homogenization

In this paper, we establish a general convergence theorem for solutions of multivariate stochastic differential equations with countably many singular terms expressed as integrals with respect to local times. The processes under consideration describe diffusions in the presence of semipermeable hyperplane interfaces. These interfaces may become sticky after applying a random time change that depends on the amount of local time accumulated on each interface. We show that, as the distance between the interfaces tends to zero, the local-time terms converge to a limiting homogenized drift term. When the interfaces are sticky, the limiting diffusion also decelerates, meaning that its diffusion coefficient is effectively reduced. Such limit theorems illustrate a form of stochastic homogenization for diffusions evolving in a heterogeneous medium interleaved with semipermeable, sticky interfaces.

math.PR

How to simulate Lévy flights in a steep potential: An explicit splitting numerical scheme

We propose an effective explicit numerical scheme for simulating solutions of stochastic differential equations with confining superlinear drift terms, driven by multiplicative heavy-tailed Lévy noise. The scheme is designed to prevent explosion and accurately capture all finite moments of the solutions. In the purely Gaussian case, it correctly reproduces moments of sub-Gaussian tails of the solutions. This method is particularly well-suited for approximating statistical moments and other probabilistic characteristics of Lévy flights in steep potential landscapes.

physics.comp-ph

Heterogeneous diffusion process with power-law nonlinearity

In this paper, we study solutions of the heterogeneous diffusion process with power-law nonlinearity governed by the stochastic differential equation $\mathrm{d}X_t= |X_t|^α\,\mathrm{d}B_t + αλ|X_t|^{2α-1}\operatorname{sign}(X_t)\,\mathrm{d}t$, where $α\in (0,1)$ and $λ\in[0,1]$. The parameter $α$ controls the nonlinear power-law profile of the diffusion coefficient, while the parameter $λ$ specifies the interpretation of the stochastic integral in the pre-equation $\dot X=|X|^α\dot B$. We demonstrate that the solutions of this equation can be represented as nonlinear transformations of a skew Bessel process with dimension $δ\in \mathbb{R}$.

math.PR

Strong uniform Wong--Zakai approximations of Lévy-driven Marcus SDEs

For a solution $X$ of a Lévy-driven $d$-dimensional Marcus (canonical) stochastic differential equation, we show that the Wong--Zakai type approximation scheme $X^h$ has a strong convergence of order $\frac12$: for each $T\in [0,\infty)$ and all $x\in\mathbb R^d$ we have $$ \mathbf E \sup_{kh\leq T}|X_{kh}(x)-X^h_{kh}(x)|\leq C h^{\frac{1}{2}}(1+|x|),\quad h\to 0. $$ We also determine the rate of the locally uniform strong convergence: for each $N\in(0,\infty)$ and $\varepsilon\in (0,1)$ we have $$ \mathbf E\sup_{|x|\leq N}\sup_{kh\leq T}|X_{kh}(x)-X^h_{kh}(x)|\leq C h^{\frac{1-\varepsilon}{4d}},\quad h\to 0. $$

math.PR

Walsh's Brownian Motion and Donsker Scaling Limits of Perturbed Random Walks

In this paper we study Markov chains with the state space given by the coordinate axes of $\mathbb R^m$, $m \geq 2$, whose step sizes on each positive half-axis are distributed according to a centered probability distribution with variance $v_i^2 \in (0, \infty)$, $i = 1,\ldots, m$. Under very mild assumptions on the jumps sizes on the negative half-axes, we show that the Donsker scaling limit of such Markov chains is a Walsh Brownian motion whose weights are determined explicitly in terms of stationary distributions of certain embedded Markov chains. This convergence result is applied to integer-valued random walks perturbed on a finite subset of $\mathbb Z$ called a membrane. We show that their Donsker scaling limit is an oscillating skew Brownian motion.

math.PR

Stochastic selection problem for a Stratonovich SDE with power non-linearity

In our paper [Bernoulli 26(2), 2020, 1381-1409], we found all strong Markov solutions that spend zero time at $0$ of the Stratonovich stochastic differential equation $d X=|X|^α\circ dB$, $α\in (0,1)$. These solutions have the form $X_t^θ=F(B^θ_t)$, where $F(x)=\frac{1}{1-α}|x|^{1/(1-α)}\text{sign}\, x$ and $B^θ$ is the skew Brownian motion with skewness parameter $θ\in [-1,1]$ starting at $F^{-1}(X_0)$. In this paper we show how an addition of small external additive noise $\varepsilon W$ restores uniqueness. In the limit as $\varepsilon\to 0$, we recover heterogeneous diffusion corresponding to the physically symmetric case $θ=0$.

math.PR

Homogenization of a multivariate diffusion with semipermeable interfaces

We study the homogenization problem for a system of stochastic differential equation with local time terms that models a multivariate diffusion in presence of semipermeable hyperplane interfaces with oblique penetration. We show that this system has a unique weak solution and determine its weak limit as the distances between the interfaces converge to zero. In the limit, the singular local times terms vanish and give rise to an additional regular interface-induced drift.

math.PR

Cutoff ergodicity bounds in Wasserstein distance for a viscous energy shell model with Lévy noise

This article establishes explicit non-asymptotic ergodic bounds in the renormalized Wasserstein-Kantorovich-Rubinstein (WKR) distance for a viscous energy shell lattice model of turbulence with random energy injection. The system under consideration is driven either by a Brownian motion, a symmetric $α$-stable Lévy process, a stationary Gaussian or $α$-stable Ornstein-Uhlenbeck process, or by a general Lévy process with second moments. The obtained non-asymptotic bounds establish asymptotically abrupt thermalization. The analysis is based on the explicit representation of the solution of the system in terms of convolutions of Bessel functions.

math-ph

Early warning signs of critical transitions -- The $α$-stable case

Statistical early warning signs can be used to identify an approaching bifurcation in stochastic dynamical systems and are now regularly employed in applications concerned with the identification of potential rapid, non-linear change or tipping points. However, the reliability of these early warning signs relies on a number of key mathematical assumptions, most notably the presence of Gaussian noise. We here show that for systems driven by non-Gaussian, $α$-stable noise, the classical early warning signs of rising variance and autocorrelation are not supported by mathematical theory and their use poses the danger of spurious, false-positive results. To address this, we provide a generalized approach by introduce the scaling factor $γ_X$ as an alternative early warning sign. We show that in the case of the Ornstein-Uhlenbeck process, there exists a direct inverse relationship between $γ_{X}$ and the bifurcation parameter, telling us that $γ_{X}$ will increase as we approach the bifurcation. Our numerical simulations confirm theoretical results and show that our findings generalize well to non-linear, non-equilibrium systems. We thus provide a generalized, robust and applicable statistical early warning sign for systems driven by Gaussian and non-Gaussian $α$-stable noise.

math.DS

Stochastic energy-balance model with a moving ice line

In [SIAM J. Appl. Dyn. Sys., 12(4):2068--2092, 2013], Widiasih proposed and analyzed a deterministic one-dimensional Budyko-Sellers energy-balance model with a moving ice-line. In this paper, we extend this model to the stochastic setting and analyze it within the framework of stochastic slow-fast systems. We derive the dynamics for the ice line in the limit of a small parameter as a solution to a stochastic differential equation. The stochastic approach enables the study of co-existing (metastable) climate states as well as the transition dynamics between them.

math.PR

First Order Linear Marcus SPDEs

In this paper we solve a Lévy driven linear stochastic first order partial differential equation (transport equation) understood in the canonical (Marcus) form. The solution can be obtained with the help of the method of stochastic characteristics. It has the same form as a solution of a deterministic PDE or a solution of a stochastic PDE driven by a Brownian motion studied by Kunita (1984, 1997).

math.PR

Moment bounds for dissipative semimartingales with heavy jumps

In this paper we show that if large jumps of an Itô-semimartingale $X$ have a finite $p$-moment, $p>0$, the radial part of its drift is dominated by $-|X|^κ$ for some $κ\geq -1$, and the balance condition $p+κ>1$ holds true, then under some further natural technical assumptions $\sup_{t\geq 0} \mathbf{E} |X_t|^{p_X}<\infty$ for each $p_X\in(0,p+κ-1)$. The upper bound $p+κ-1$ is generically optimal. The proof is based on the extension of the method of Lyapunov functions to the semimartingale framework. The uniform moment estimates obtained in this paper are indispensable for the analysis of ergodic properties of Lévy driven stochastic differential equations and Lévy driven multi-scale systems.

math.PR

Generalized selection problem with Lévy noise

Let $A_\pm>0$, $β\in(0,1)$, and let $Z^{(α)}$ be a strictly $α$-stable Lévy process with the jump measure $ν(\mathrm{d} z)=(C_+\mathbb{I}_{(0,\infty)}(z)+ C_-\mathbb{I}_{(-\infty,0)}(z))|z|^{-1-α}\,\mathrm{d} z$, $α\in (1,2)$, $C_\pm\geq 0$, $C_++C_->0$. The selection problem for the model stochastic differential equation $\mathrm{d} \bar X^\varepsilon=(A_+\mathbb{I}_{[0,\infty)}(\bar X^\varepsilon) - A_-\mathbb{I}_{(-\infty,0)}(\bar X^\varepsilon))|\bar X^\varepsilon|^β\,\mathrm{d} t +\varepsilon \mathrm{d} Z^{(α)}$ states that in the small noise limit $\varepsilon\to 0$, solutions $\bar X^\varepsilon$ converge weakly to the maximal or minimal solutions of the limiting non-Lipschitzian ordinary differential equation $\mathrm{d} \bar x=(A_+\mathbb{I}_{[0,\infty)}(\bar x)- A_-\mathbb{I}_{(\infty,0)}(\bar x))|\bar x|^β\,\mathrm{d} t$ with probabilities $\bar p_\pm=\bar p_\pm(α,C_+/C_-,β, A_+/A_-)$, see [Pilipenko and Proske, Stat. Probab. Lett., 132:62-73, 2018]. In this paper we solve the generalized selection problem for the stochastic differential equation $\mathrm{d} X^\varepsilon=a(X^\varepsilon)\,\mathrm{d} t+\varepsilon b(X^\varepsilon)\,\mathrm{d} Z$ whose dynamics in the vicinity of the origin in certain sense reminds of dynamics of the model equation. In particular we show that solutions $X^\varepsilon$ also converge to the maximal or minimal solutions of the limiting irregular ordinary differential equation $\mathrm{d} x=a(x) \,\mathrm{d} t$ with the same model selection probabilities $\bar p_\pm$. This means that for a large class of irregular stochastic differential equations, the selection dynamics is completely determined by four local parameters of the drift and the jump measure.

math.PR

Drift Estimation for a Lévy-Driven Ornstein-Uhlenbeck Process with Heavy Tails

We consider the problem of estimation of the drift parameter of an ergodic Ornstein--Uhlenbeck type process driven by a Lévy process with heavy tails. The process is observed continuously on a long time interval $[0,T]$, $T\to\infty$. We prove that the statistical model is locally asymptotic mixed normal and the maximum likelihood estimator is asymptotically efficient.

math.ST