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In this paper, we study dimension reduction techniques for large-scale controlled stochastic differential equations (SDEs). The drift of the considered SDEs contains a polynomial term satisfying a one-sided growth condition. Such…

Probability · Mathematics 2023-03-10 Martin Redmann

We present a nonlinear dynamical approximation method for time-dependent Partial Differential Equations (PDEs). The approach makes use of parametrized decoder functions, and provides a general, and principled way of understanding and…

Numerical Analysis · Mathematics 2025-05-20 Daan Bon , Benjamin Caris , Olga Mula

In this paper we prove well-posedness and stabibility of a class of stochastic delay differential equations with singular drift. Moreover, we show local well-posedness under localized assumptions.

Probability · Mathematics 2017-08-04 Stefan Bachmann

Stabilized explicit methods are particularly efficient for large systems of stiff stochastic differential equations (SDEs) due to their extended stability domain. However, they loose their efficiency when a severe stiffness is induced by…

Numerical Analysis · Mathematics 2021-08-13 Assyr Abdulle , Giacomo Rosilho de Souza

Building on results obtained in [GVRS], we prove Local Stable and Unstable Manifold Theorems for nonlinear, singular stochastic delay differential equations. The main tools are rough paths theory and a semi-invertible Multiplicative Ergodic…

Probability · Mathematics 2020-03-09 Mazyar Ghani Varzaneh , Sebastian Riedel

Monotone systems comprise an important class of dynamical systems that are of interest both for their wide applicability and because of their interesting mathematical properties. It is known that under the property of quasimonotonicity…

Dynamical Systems · Mathematics 2015-05-20 Eoin Devane , Ioannis Lestas

To model time series accurately is important within a wide range of fields. As the world is generally too complex to be modelled exactly, it is often meaningful to assess the probability of a dynamical system to be in a specific state. This…

Machine Learning · Computer Science 2023-03-16 Mari Dahl Eggen , Alise Danielle Midtfjord

We show the continuous dependence of solutions of linear nonautonomous second order parabolic partial differential equations (PDEs) with bounded delay on coefficients and delay. The assumptions are very weak: only convergence in the weak-*…

Analysis of PDEs · Mathematics 2022-07-19 Marek Kryspin , Janusz Mierczyński

We extend the notion of mean-field SDEs to SDEs driven by $G$-Brownian motion. More precisely, we consider a $G$-SDE where the coefficients depend not only on time and the current state but also on the solution as random variable.

Probability · Mathematics 2025-08-06 Karl-Wilhelm Georg Bollweg , Thilo Meyer-Brandis

This article proposes a non-autonomous mathematical model with delay for confrontation between two countries, and examines the stability of its equilibrium state. Our criteria for stability take into account the influence of the factor of…

Dynamical Systems · Mathematics 2026-05-14 Teresa Faria , Anatoliy A. Martynyuk

The location of roots of the characteristic equation of a linear delay differential equation (DDE) determines the stability of the linear DDE. However, by its transcendency, there is no general criterion on the contained parameters for the…

Dynamical Systems · Mathematics 2022-06-29 Junya Nishiguchi

In this paper we consider the global stability of solutions of an affine stochastic differential equation. The differential equation is a perturbed version of a globally stable linear autonomous equation with unique zero equilibrium where…

Probability · Mathematics 2013-10-10 John A. D. Appleby , Jian Cheng , Alexandra Rodkina

This paper introduces a backward stochastic differential equation driven by both Brownian motion and a Markov chain (BSDEBM). Regime-switching is also incorporated through its driver. The existence and uniqueness of the solution of the…

Probability · Mathematics 2022-03-08 Engel John C. Dela Vega , Robert J. Elliott

In this paper, we consider the asymptotic stability for a system of linear delay differential equations. By analysing of the characteristic equation in detail, we have established the necessary and sufficient condition for the asymptotic…

Dynamical Systems · Mathematics 2025-04-03 Wataru Saito , Ikki Fukuda

In this paper we construct a framework for doing statistical inference for discretely observed stochastic differential equations (SDEs) where the driving noise has 'memory'. Classical SDE models for inference assume the driving noise to be…

Methodology · Statistics 2013-07-05 Martin Lysy , Natesh S. Pillai

Constructions of numerous approximate sampling algorithms are based on the well-known fact that certain Gibbs measures are stationary distributions of ergodic stochastic differential equations (SDEs) driven by the Brownian motion. However,…

Probability · Mathematics 2020-07-07 Lu-Jing Huang , Mateusz B. Majka , Jian Wang

We consider several classes of degenerate hyperbolic equations involving delay terms and suitable nonlinearities. The idea is to rewrite the problems in an abstract way and, using semigroup theory and energy method, we study well posedness…

Analysis of PDEs · Mathematics 2024-07-16 Alessandro Camasta , Genni Fragnelli , Cristina Pignotti

The path independence of additive functionals for SDEs driven by the G-Brownian motion is characterized by nonlinear PDEs. The main result generalizes the existing ones for SDEs driven by the standard Brownian motion.

Probability · Mathematics 2019-08-22 Panpan Ren , Fen-Fen Yang

The main contributions of this paper are three fold. First, our primary concern is to investigate a class of stochastic recursive delayed control problems which arise naturally with sound backgrounds but have not been well-studied yet. For…

Optimization and Control · Mathematics 2011-12-06 Li Chen , Jianhui Huang

Stochastic differential equations (SDEs) are one of the most important representations of dynamical systems. They are notable for the ability to include a deterministic component of the system and a stochastic one to represent random…

Machine Learning · Computer Science 2021-05-19 Noura Dridi , Lucas Drumetz , Ronan Fablet
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