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In this paper we study path-by-path uniqueness for multidimensional stochastic differential equations driven by the Brownian sheet. We assume that the drift coefficient is unbounded, verifies a spatial linear growth condition and is…

Probability · Mathematics 2022-09-27 Antoine-Marie Bogso , Moustapha Dieye , Olivier Menoukeu-Pamen

In this article we prove that stochastic differential equation (SDE) with Sobolev drift on compact Riemannian manifold admits a unique $\nu$-almost everywhere stochastic invertible flow, where $\nu$ is the Riemannian measure, which is…

Probability · Mathematics 2010-07-12 Xicheng Zhang

The classical result by It\^o on the existence of strong solutions of stochastic differential equations (SDEs) with Lipschitz coefficients can be extended to the case where the drift is only measurable and bounded. These generalizations are…

Probability · Mathematics 2021-10-05 Gunther Leobacher , Michaela Szölgyenyi , Stefan Thonhauser

In this paper we prove a viability result for multidimensional, time dependent, stochastic differential equations driven by fractional Brownian motion with Hurst parameter1/2 < H < 1, using pathwise approach. The sufficient condition is…

Dynamical Systems · Mathematics 2008-09-01 Ioana Ciotir , Aurel Rascanu

In a recent paper by the first two named authors, existence of martingale solutions to a stochastic nonlinear Schr\"odinger equation driven by a L\'evy noise was proved. In this paper, we prove pathwise uniqueness, uniqueness in law and…

Probability · Mathematics 2018-05-31 Erika Hausenblas , Anne de Bouard , Martin Ondrejat

We introduce a new method of proving pathwise uniqueness, and we apply it to the degenerate stochastic differential equation \[dX_t=|X_t|^{\alpha} dW_t,\] where $W_t$ is a one-dimensional Brownian motion and $\alpha\in(0,1/2)$. Weak…

Probability · Mathematics 2009-09-29 Richard F. Bass , Krzysztof Burdzy , Zhen-Qing Chen

We study the quenched invariance principle for random conductance models with long range jumps on $\Z^d$, where the transition probability from $x$ to $y$ is, on average, comparable to $|x-y|^{-(d+\alpha)}$ with $\alpha\in (0,2)$ but is…

Probability · Mathematics 2020-05-01 Xin Chen , Takashi Kumagai , Jian Wang

The purpose of this note is to prove that the flatness of an invariant manifold for a semilinear stochastic partial differential equation driven by L\'{e}vy processes is at least equal to the number of driving sources with small jumps. We…

Probability · Mathematics 2025-11-21 Stefan Tappe

Coupling by reflection mixed with synchronous coupling is constructed for a class of stochastic differential equations (SDEs) driven by L\'{e}vy noises. As an application, we establish the exponential contractivity of the associated…

Statistics Theory · Mathematics 2016-03-18 Jian Wang

This paper introduces time-continuous numerical schemes to simulate stochastic differential equations (SDEs) arising in mathematical finance, population dynamics, chemical kinetics, epidemiology, biophysics, and polymeric fluids. These…

Probability · Mathematics 2015-03-13 Nawaf Bou-Rabee , Eric Vanden-Eijnden

We study a multidimensional stochastic differential equation with additive noise: \[ d X_t=b(t, X_t) dt +d \xi_t, \] where the drift $b$ is integrable in space and time, and $\xi$ is either a fractional Brownian motion or a L\'evy process.…

Probability · Mathematics 2026-02-11 Oleg Butkovsky , Samuel Gallay

In this paper we study the following stochastic Hamiltonian system in ${\mathbb R}^{2d}$ (a second order stochastic differential equation), $$ d \dot X_t=b(X_t,\dot X_t)d t+\sigma(X_t,\dot X_t)d W_t,\ \ (X_0,\dot X_0)=(x,v)\in{\mathbb…

Probability · Mathematics 2017-02-08 Xicheng Zhang

In this paper, we study the weak differentiability of global strong solution of stochastic differential equations, the strong Feller property of the associated diffusion semigroups and the global stochastic flow property in which the…

Probability · Mathematics 2022-11-17 Wenjie Ye

In this paper we prove the stochastic homeomorphism flow property and the strong Feller property for stochastic differential equations with sigular time dependent drifts and Sobolev diffusion coefficients. Moreover, the local well posedness…

Probability · Mathematics 2011-05-04 Xicheng Zhang

In this paper we introduce a transformation technique, which can on the one hand be used to prove existence and uniqueness for a class of SDEs with discontinuous drift coefficient. One the other hand we present a numerical method based on…

Probability · Mathematics 2016-08-03 Gunther Leobacher , Michaela Szölgyenyi

In this work, we introduce a theory of stochastic integration with respect to symmetric $\alpha$-stable cylindrical L\'evy processes. Since $\alpha$-stable cylindrical L\'evy processes do not enjoy a semi-martingale decomposition, our…

Probability · Mathematics 2022-11-21 Gergely Bodó , Markus Riedle

We derive moment estimates and a strong limit theorem for space inverses of stochastic flows generated by jump SDEs with adapted coefficients in weighted H\"older norms using the Sobolev embedding theorem and the change of variable formula.…

Probability · Mathematics 2014-11-25 James-Michael Leahy , Remigijus Mikulevicius

We introduce a generalized notion of semilinear elliptic partial differential equations where the corresponding second order partial differential operator $L$ has a generalized drift. We investigate existence and uniqueness of generalized…

Probability · Mathematics 2015-06-03 Francesco Russo , Lukas Wurzer

This paper studies the numerical approximation for McKean-Vlasov stochastic differential equations driven by L\'evy processes. We propose a tamed-adaptive Euler-Maruyama scheme and consider its strong convergence in both finite and infinite…

Probability · Mathematics 2024-01-09 Ngoc Khue Tran , Trung-Thuy Kieu , Duc-Trong Luong , Hoang-Long Ngo

In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic time series which has big jump properties. Our contributions…

Machine Learning · Computer Science 2022-11-04 Luxuan Yang , Ting Gao , Yubin Lu , Jinqiao Duan , Tao Liu