Searcharxiv⌕ Search

arXiv · 2610.29648

Wave equation with unbounded terms driven by a stochastic measure

Abstract

The one-dimensional wave equation with additive noise driven by a stochastic measure is considered. For the stochastic integrator, we assume only the sigma-additivity in probability and that $|y|^τ$ is integrable on $\mathbb{R}$ for some $τ>1/2$. The class of stochastic measures includes many important cases of random processes. In the equation, the stochastic measure depends only on the spatial variable. The existence, uniqueness, and Hölder regularity of the mild solution are proved. In the previous publications, similar results were obtained for the case of bounded terms of the wave equation driven by a stochastic measure. In our paper, one term may be unbounded, satisfying the linear growth condition. Nevertheless, we prove that the solution is bounded in this case.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mykhailo Debeliak, Vadym Radchenko. 2026-10-21. Wave equation with unbounded terms driven by a stochastic measure. https://arxiv.org/abs/2610.29648

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

KEEP EXPLORING

Related papers

The maximum distance covariance of bounded random vectors

A sharp upper bound is obtained for the distance covariance of bounded random vectors in terms of the diameters of their supports alone. The bound is attained for all pairs of support diameter constraints and hence provides the maximum distance covariance subject to constraints of this kind. A normalized measure of dependence is derived by dividing distance covariance by the sharp bound when it is positive. Several properties of the resulting quotient are proved, including domination by distance correlation, invariance under similarities, and subadditivity under sums of independent pairs. The quotient is shown to be nonincreasing under the addition of independent noise, a property not shared by distance correlation. The usual sample distance covariance inherits the sharp bound from the population quantity and the associated normalized statistic is strongly consistent.

math.PR↗

Ergodicity for stochastic neural field equations

We investigate the well-posedness and long-time behavior of a general continuum neural field model with Gaussian noise on possibly unbounded domains. In particular, we give conditions for the existence of invariant probability measures by restricting the solution flow to an invariant subspace with a nonlocal metric. Under the assumption of a sufficiently large decay parameter relative to the noise intensity, the norm of the connectivity kernel operator, and the Lipschitz regularity of the activation function, we establish exponential ergodicity and exponential mixing of the associated Markovian Feller semigroup and the uniqueness of the invariant measure.

math.PR↗

On a class of multiplicative Lindley-type recursions with Markov-modulated dependencies

In this paper, we study a class of Markov-modulated multiplicative Lindley-type recursions of the form $W_{n+1}=[V_nW_n+Y_n(V_n)]^+$, where the additive term may depend on the multiplicative coefficient and on a common finite-state background Markov chain. The main feature of the model is that the multiplicative coefficient is allowed to take negative values, which destroys the usual monotonicity structure of Lindley-type recursions. We consider two cases. In the first one, $V_n$ may take the values $1$, a constant $a\in(0,1)$, or a negative value, according to specific probabilities, while $Y_n(V_n)=S_n-A_{n+1}$ and the relevant Laplace--Stieltjes transforms are assumed to be rational. In the second one, $V_n\in\{1,-1\}$, and the additive term has a more general form depending on $V_n$. For both models, we derive vector-valued functional equations for the stationary transform. For the first model, we obtain a recursive transform representation, while for the second model we derive a finite-dimensional characterization based on root-cancellation conditions. We also provide a recursive procedure for computing stationary moments and discuss the light-tailed asymptotic behaviour. An extension based on the Farlie--Gumbel--Morgenstern copula is also presented. Numerical experiments show how the transform representation can be used to compute stationary moments, tail probabilities and quantiles, and validate the results against Monte Carlo simulation.

math.PR↗