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Related papers: Forward Backward SDEs in Weak Formulation

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We discuss regular and weak solutions to rough partial differential equations (RPDEs), thereby providing a (rough path-)wise view on important classes of SPDEs. In contrast to many previous works on RPDEs, our definition gives honest…

Probability · Mathematics 2019-02-11 Joscha Diehl , Peter K. Friz , Wilhelm Stannat

The Feynman-Kac equations are a type of partial differential equations describing the distribution of functionals of diffusive motion. The probability density function (PDF) of Brownian functionals satisfies the Feynman-Kac formula, being a…

Computational Physics · Physics 2015-02-03 Weihua Deng , Minghua Chen , Eli Barkai

In this paper, we study the numerical method for solving forward-backward stochastic differential equations driven by $G$-Brownian motion ($G$-FBSDEs) which correspond to fully nonlinear partial differential equations (PDEs). First, we give…

Numerical Analysis · Mathematics 2022-05-19 Mingshang Hu , Lianzi Jiang

In this paper, we consider forward-backward stochastic differential equation driven by $G$-Brownian motion ($G$-FBSDEs in short) with small parameter $\varepsilon > 0$. We study the asymptotic behavior of the solution of the backward…

Probability · Mathematics 2020-03-27 Ibrahim Dakaou , Abdoulaye Soumana Hima

Two novel numerical estimators are proposed for solving forward-backward stochastic differential equations (FBSDEs) appearing in the Feynman-Kac representation of the value function in stochastic optimal control problems. In contrast to the…

Optimization and Control · Mathematics 2021-10-01 Kelsey P. Hawkins , Ali Pakniyat , Panagiotis Tsiotras

Applications in quantitative finance such as optimal trade execution, risk management of options, and optimal asset allocation involve the solution of high dimensional and nonlinear Partial Differential Equations (PDEs). The connection…

Machine Learning · Statistics 2019-10-28 Batuhan Güler , Alexis Laignelet , Panos Parpas

We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM). SDEs offer a versatile tool for modeling real-world…

Machine Learning · Computer Science 2023-10-20 Rembert Daems , Manfred Opper , Guillaume Crevecoeur , Tolga Birdal

In this paper, we establish the existence and uniqueness of fully coupled forward-backward stochastic differential equations (FBSDEs in short) driven by anomalous sub-diffusions $B_{L_t}$ under suitable monotonicity conditions on the…

Probability · Mathematics 2023-11-28 Shuaiqi Zhang , Zhen-Qing Chen

In this paper, we consider a system of forward-backward stochastic differential equations (FBSDEs) with monotone functionals. We show the existence and uniqueness of such a system by the method of continuation similarly to Peng and Wu…

Probability · Mathematics 2018-08-07 Saran Ahuja , Weiluo Ren , Tzu-Wei Yang

In this paper, we consider the Cauchy problem of semi-linear degenerate backward stochastic partial differential equations (BSPDEs in short) under general settings without technical assumptions on the coefficients. For the solution of…

Probability · Mathematics 2011-09-06 Kai Du , Qi Zhang

Stochastic averaging for a class of backward stochastic differential equations driven by both standard and fractional Brownian motions (SFrBSDEs in short), is investigated. An averaged SFrBSDEs for the original SFrBSDEs is proposed, and…

Probability · Mathematics 2021-06-04 Ibrahima Faye , Sadibou Aidara , Yaya Sagna

We demonstrate that backward stochastic differential equations (BSDE) may be reformulated as ordinary functional differential equations on certain path spaces. In this framework, neither It\^{o}'s integrals nor martingale representation…

Probability · Mathematics 2012-11-20 Gechun Liang , Terry Lyons , Zhongmin Qian

This paper is dedicated to the presentation and the analysis of a numerical scheme for forward-backward SDEs of the McKean-Vlasov type, or equivalently for solutions to PDEs on the Wasserstein space. Because of the mean field structure of…

Probability · Mathematics 2017-03-07 Jean-François Chassagneux , Dan Crisan , François Delarue

In this paper, a Feynman-Kac formula is established for stochastic partial differential equation driven by Gaussian noise which is, with respect to time, a fractional Brownian motion with Hurst parameter $H<1/2$. To establish such a…

Probability · Mathematics 2012-05-24 Yaozhong Hu , Fei Lu , David Nualart

In this paper, we study a class of stochastic differential equations with additive noise that contains a fractional Brownian motion (fBM) and a Poisson point process of class (QL). The differential equation of this kind is motivated by the…

Probability · Mathematics 2015-04-14 Lihua Bai , Jin Ma

This paper is concerned with the relationship between forward-backward stochastic Volterra integral equations (FBSVIEs, for short) and a system of (non-local in time) path dependent partial differential equations (PPDEs, for short). Due to…

Probability · Mathematics 2021-01-26 Hanxiao Wang , Jiongmin Yong , Jianfeng Zhang

In this paper, we propose a weak formulation of the singular diffusion equation subject to the dynamic boundary condition. The weak formulation is based on a reformulation method by an evolution equation including the subdifferential of a…

Analysis of PDEs · Mathematics 2017-05-09 Ryota Nakayashiki , Ken Shirakawa

In this paper, we obtain the existence and uniqueness theorem of $L^{p}$-solution for coupled forward-backward stochastic differential equations driven by G-Brownian motion (G-FBSDEs) with arbitrary $T$ under weakly coupling condition.…

Probability · Mathematics 2022-11-29 Xiaojuan Li

The Feynman-Kac formulae (FKF) express local solutions of partial differential equations (PDEs) as expectations with respect to some complementary stochastic differential equation (SDE). Repeatedly sampling paths from the complementary SDE…

Methodology · Statistics 2016-03-15 Jake Carson , Murray Pollock , Mark Girolami

In the framework of stochastic functional differential equations (SFDE's) and the corresponding calculus developed in the recent years by F. Yan and S. Mohammed, we provide a series of representation formulae for a variety of highly…

Probability · Mathematics 2016-02-29 Stefano Belloni