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In recent years, SPDEs have become a well-studied field in mathematics. With their increase in popularity, it becomes important to efficiently approximate their solutions. Thus, our goal is a contribution towards the development of…

Numerical Analysis · Mathematics 2024-01-17 Evelyn Buckwar , Ana Djurdjevac , Monika Eisenmann

This work presents a complete geometrical characterisation of divisible and indivisible time-evolution at the level of probabilities for systems with two configurations, open or closed. Our new geometrical construction in the space of…

Quantum Physics · Physics 2025-05-14 Leandro Silva Pimenta

This work continues a line of works on developing partially explicit methods for multiscale problems. In our previous works, we have considered linear multiscale problems, where the spatial heterogeneities are at subgrid level and are not…

Numerical Analysis · Mathematics 2021-08-31 Eric T. Chung , Yalchin Efendiev , Wing Tat Leung , Wenyuan Li

Many time-dependent linear partial differential equations of mathematical physics and continuum mechanics can be phrased in the form of an abstract evolutionary system defined on a Hilbert space. In this paper we discuss a general framework…

Analysis of PDEs · Mathematics 2019-05-09 Stefan Neukamm , Mario Varga , Marcus Waurick

We develop a complete and rigorous mathematical framework for the analysis of stochastic neural field equations under the influence of spatially extended additive noise. By comparing a solution to a fixed deterministic front profile it is…

Probability · Mathematics 2019-02-11 Jennifer Krüger , Wilhelm Stannat

A unification of characteristic mode decomposition for all method-of-moment formulations of field integral equations describing free-space scattering is derived. The work is based on an algebraic link between impedance and transition…

Classical Physics · Physics 2023-01-04 Mats Gustafsson , Lukas Jelinek , Kurt Schab , Miloslav Capek

Inspired by the works of \cite{baz2} and \cite{kian}, this study develops an abstract framework for analyzing differential equations with space-dependent fractional time derivatives and bounded operators. Within this framework, we establish…

Analysis of PDEs · Mathematics 2025-02-19 Tiago Augusto dos Santos Boza , Paulo Mendes de Carvalho Neto

Multistage stochastic optimization problems are, by essence, complex as their solutions are indexed both by stages and by uncertainties. Their large scale nature makes decomposition methods appealing, like dynamic programming which is a…

Optimization and Control · Mathematics 2023-05-01 Pierre Carpentier , Jean-Philippe Chancelier , Michel de Lara , Thomas Martin , Tristan Rigaut

This work is concerned with the stability properties of linear stochastic differential equations with random (drift and diffusion) coefficient matrices, and the stability of a corresponding random transition matrix (or exponential…

Probability · Mathematics 2019-05-02 Adrian N. Bishop , Pierre Del Moral

We demonstrate that in three space dimensions, the scattering behaviour of semilinear wave equations with quintic-type nonlinearities uniquely determines the nonlinearity. The nonlinearity is permitted to depend on both space and time.

Analysis of PDEs · Mathematics 2025-01-29 Nicholas Hu , Rowan Killip , Monica Visan

Retarded stochastic differential equations (SDEs) constitute a large collection of systems arising in various real-life applications. Most of the existing results make crucial use of dissipative conditions. Dealing with "pure delay" systems…

Probability · Mathematics 2013-08-12 Jianhai Bao , George Yin , Chenggui Yuan

This paper studies the stochastic differential equation (SDE) associated to a two-level quantum system (qubit) subject to Hamiltonian evolution as well as unmonitored and monitored decoherence channels. The latter imply a stochastic…

Quantum Physics · Physics 2017-06-28 Alain Sarlette , Pierre Rouchon

We present a condition for a stochastic differential equation dX_{t}={\mu}(t,X_{t})dt+{\sigma}(t,X_{t})dB_{t} to have a unique functional solution of the form Z(t,B_{t}). The condition expresses a relation between {\mu} and {\sigma}. A…

Probability · Mathematics 2012-09-05 Imme van den Berg

Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advection-diffusion partial differential equation provides a…

Methodology · Statistics 2016-02-18 Fabio Sigrist , Hans R. Künsch , Werner A. Stahel

In this paper, we show that the exponential integrator scheme both in spatial discretization and time discretization for a class of stochastic partial differential equations has a unique stationary distribution whenever the stepsize is…

Probability · Mathematics 2013-03-08 Jianhai Bao , Chenggui Yuan

A system of partial differential equations representing stochastic neural fields was recently proposed with the aim of modelling the activity of noisy grid cells when a mammal travels through physical space. The system was rigorously…

Analysis of PDEs · Mathematics 2023-07-18 José Antonio Carrillo , Pierre Roux , Susanne Solem

This paper proves a version for stochastic differential equations of the Lie-Scheffers Theorem. This result characterizes the existence of nonlinear superposition rules for the general solution of those equations in terms of the involution…

Probability · Mathematics 2008-03-06 Joan-Andreu Lázaro-Camí , Juan-Pablo Ortega

We prove that distribution dependent (also called McKean--Vlasov) stochastic delay equations of the form \begin{equation*} \mathrm{d}X(t)= b(t,X_t,\mathcal{L}_{X_t})\mathrm{d}t+ \sigma(t,X_t,\mathcal{L}_{X_t})\mathrm{d}W(t) \end{equation*}…

Probability · Mathematics 2020-05-18 Rico Heinemann

Shift Harnack and integration by part formula are establish for semilinear spde with delay and a class of stochastic semilinear evolution equation which cover the hyperdissipative Naiver-Stokes/Burges equation. For the case of stochastic…

Probability · Mathematics 2012-11-13 Shao-Qin Zhang

The construction of stochastic solutions for nonlinear partial differential equations is a powerful method to obtain new exact results and to develop efficient numerical algorithms, in particular when domain decomposition techniques are…

Mathematical Physics · Physics 2012-09-17 Rui Vilela Mendes