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We consider a degenerate infinite dimensional stochastic Hamiltonian system with multiplicative noise and establish the essential m-dissipativity on $L^2(\mu^{\Phi})$ of the corresponding Kolmogorov (backwards) operator. Here, $\Phi$ is the…

Probability · Mathematics 2024-10-22 Benedikt Eisenhuth , Martin Grothaus

The aim of this article is to construct solutions to second order in time stochastic partial differential equations and to show hypocoercivity of the corresponding transition semigroups. More generally, we analyze non-linear…

Probability · Mathematics 2023-06-21 Benedikt Eisenhuth , Martin Grothaus

We provide a complete elaboration of the $L^2$-Hilbert space hypocoercivity theorem for the degenerate Langevin dynamics with multiplicative noise, studying the longtime behaviour of the strongly continuous contraction semigroup solving the…

Functional Analysis · Mathematics 2022-05-25 Alexander Bertram , Martin Grothaus

In this article we extend the modern, powerful and simple abstract Hilbert space strategy for proving hypocoercivity that has been developed originally by Dolbeault, Mouhot and Schmeiser. As well-known, hypocoercivity methods imply an…

Functional Analysis · Mathematics 2015-12-31 Martin Grothaus , Patrik Stilgenbauer

We employ weak hypocoercivity methods to study the long-term behavior of operator semigroups generated by degenerate Kolmogorov operators with variable second-order coefficients, which solve the associated abstract Cauchy problem. We prove…

Probability · Mathematics 2021-10-13 Alexander Bertram , Martin Grothaus

We provide a complete elaboration of the $L^2$-Hilbert space hypocoercivity theorem for the degenerate Langevin dynamics via studying the longtime behavior of the strongly continuous contraction semigroup solving the associated Kolmogorov…

Functional Analysis · Mathematics 2016-08-30 Martin Grothaus , Patrik Stilgenbauer

First essential m-dissipativity of an infinite-dimensional Ornstein-Uhlenbeck operator $N$, perturbed by the gradient of a potential, on a domain $\mathcal{F}C_b^{\infty}$ of finitely based, smooth and bounded functions, is shown. Our…

Functional Analysis · Mathematics 2021-04-13 Benedikt Eisenhuth , Martin Grothaus

We consider the nonlinear Kolmogorov equation posed in a Hilbert space $H$, not necessarily of finite dimension. This model was recently studied by Cox et al. [24] in the framework of weak convergence rates of stochastic wave models. Here,…

Probability · Mathematics 2022-07-06 Javier Castro

This work is concerned with the development of a family of Galerkin finite element methods for the classical Kolmogorov's equation. Kolmogorov's equation serves as a sufficiently rich, for our purposes, model problem for kinetic-type…

Numerical Analysis · Mathematics 2020-12-18 Emmanuil H. Georgoulis

In this article we develop a new abstract strategy for proving ergodicity with explicit computable rate of convergence for diffusions associated with a degenerate Kolmogorov operator L. A crucial point is that the evolution operator L may…

Functional Analysis · Mathematics 2016-01-04 Martin Grothaus , Patrik Stilgenbauer

The aim of the present paper is to study the regularity properties of the solution of a backward stochastic differential equation with a monotone generator in infinite dimension. We show some applications to the nonlinear Kolmogorov…

Probability · Mathematics 2008-04-10 Philippe Briand , Fulvia Confortola

Given a real and separable Hilbert space H we consider the measure-valued equation \begin{equation*} \int_H\phi(x)\mu_t(dx)- \int_H\phi(x)\mu(dx)= \int_0^t(\int_HK_0\phi(x)\mu_s(dx))ds, \end{equation*} where K_0 is the Kolmogorov…

Analysis of PDEs · Mathematics 2007-07-24 Luigi Manca

We propose a new stabilised finite element method for the classical Kolmogorov equation. The latter serves as a basic model problem for large classes of kinetic-type equations and, crucially, is characterised by degenerate diffusion. The…

Numerical Analysis · Mathematics 2024-12-31 Zhaonan Dong , Emmanuil H. Georgoulis , Philip J. Herbert

We consider a Volterra convolution equation in $\mathbb{R}^d$ perturbed with an additive fractional Brownian motion of Riemann-Liouville type with Hurst parameter $H\in (0,1)$. We show that its solution solves a stochastic partial…

Probability · Mathematics 2023-09-26 Alessandro Bondi , Franco Flandoli

Semilinear, $N-$dimensional stochastic differential equations (SDEs) driven by additive L\'evy noise are investigated. Specifically, given $\alpha\in\left(\frac{1}{2},1\right)$, the interest is on SDEs driven by $2\alpha-$stable,…

Probability · Mathematics 2022-10-07 Alessandro Bondi

We establish strong Feller property and irreducibility for the transition semigroup associated to a class of nonlinear stochastic partial differential equations with multiplicative degenerate noise. As a by-product, we prove uniqueness of…

Probability · Mathematics 2026-04-01 Luca Scarpa , Margherita Zanella

In this article, we propose a finite volume discretization of a one dimensional nonlinear reaction kinetic model proposed in [Neumann, Schmeiser, Kint. Rel. Mod. 2016], which describes a 2-species recombination-generation process.…

Numerical Analysis · Mathematics 2024-03-08 Marianne Bessemoulin-Chatard , Tino Laidin , Thomas Rey

We study a stochastic equation modeling the lay-down of fibers in the production process of nonwovens. The equation can be formulated as some manifold-valued Stratonovich stochastic differential equation. Especially, we study the long time…

Probability · Mathematics 2012-03-21 Martin Grothaus , Axel Klar , Johannes Maringer , Patrik Stilgenbauer

We study a reaction-diffusion evolution equation perturbed by a space-time L\'evy noise. The associated Kolmogorov operator is the sum of the infinitesimal generator of a $C_0$-semigroup of strictly negative type acting in a Hilbert space…

Probability · Mathematics 2012-12-27 Sergio Albeverio , Elisa Mastrogiacomo , Boubaker Smii

Inverse problems in scientific computing often require optimization over infinite-dimensional Hilbert spaces. A commonly used solver in such settings is stochastic gradient descent (SGD), where gradients are approximated using randomly…

Optimization and Control · Mathematics 2026-04-14 Sandra Cerrai , Qin Li , Anjali Nair , Jaeyoung Yoon
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