SearcharxivSearch

arXiv · 2506.06766

Well-posedness of Fractional Stochastic p-Laplace Equations Driven by Superlinear Transport Noise

Abstract

In this paper, we prove the existence and uniqueness of solutions of the fractional p-Laplace equation with a polynomial drift of arbitrary order driven by superlinear transport noise. By the monotone argument, we first prove the existence and uniqueness of solutions of an abstract stochastic differential equation satisfying a fully local monotonicity condition. We then apply the abstract result to the fractional stochastic p-Laplace equation defined in a bounded domain. The main difficulty is to establish the tightness as well as the uniform integrability of a sequence of approximate solutions defined by the Galerkin method. To obtain the necessary uniform estimates, we employ the Skorokhod-Jakubowski representation theorem on a topological space instead of a metric space. Since the strong Skorokhod representation theorem is incorrect even in a complete separable metric space, we pass to the limit of stochastic integrals with respect to a sequence of Wiener processes by a weak convergence argument.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bixiang Wang. 2025-06-07. Well-posedness of Fractional Stochastic p-Laplace Equations Driven by Superlinear Transport Noise. https://arxiv.org/abs/2506.06766

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR