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The classical Feynman-Kac formula states the connection between linear parabolic partial differential equations (PDEs), like the heat equation, and expectation of stochastic processes driven by Brownian motion. It gives then a method for…

Probability · Mathematics 2014-09-03 Huyen Pham

A complex notion of backward stochastic differential equation (BSDE) is proposed in this paper to give a probabilistic interpretation for linear first order complex partial differential equation (PDE). By the uniqueness and existence of…

Probability · Mathematics 2015-05-15 Yuhong Xu

It is known that Markovian forward-backward stochastic differential equations provide nonlinear Feynman-Kac representation formulae for semilinear parabolic PDEs. We show that non-Markovian forward-backward stochastic differential equations…

Probability · Mathematics 2013-06-19 Andrea Cosso

In this paper, a class of non-Markovian forward-backward doubly stochastic systems is studied. By using the technique of functional It\^o (or path-dependent) calculus, the relationship between the systems and related path-dependent…

Probability · Mathematics 2022-06-14 Yufeng Shi , Jiaqiang Wen , Jie Xiong

We propose a nonlinear forward Feynman-Kac type equation, which represents the solution of a non-conservative semilinear parabolic Partial Differential Equations (PDE). We show in particular existence and uniqueness. The solution of that…

Probability · Mathematics 2018-10-05 Anthony Lecavil , Anthony Le Cavil , Nadia Oudjane , Francesco Russo

The classical Feynman-Kac identity represents solutions of linear partial differential equations in terms of stochastic differential euqations. This representation has been generalized to nonlinear partial differential equations on the one…

Probability · Mathematics 2023-10-30 Martin Hutzenthaler , Katharina Pohl

We study a class of backward doubly stochastic differential equations (BDSDEs) involving martingales with spatial parameters, and show that they provide probabilistic interpretations (Feynman-Kac formulae) for certain semilinear stochastic…

Probability · Mathematics 2017-12-05 Jian Song , Xiaoming Song , Qi Zhang

The paper concerns classical solution of path-dependent partial differential equations (PPDEs) with coefficients depending on both variables of path and path-valued measure, which are crucial to understanding large-scale mean-field…

Probability · Mathematics 2024-07-26 Shanjian Tang , Huilin Zhang

In this paper, we establish the relationship between backward stochastic Volterra integral equations (BSVIEs, for short) and a kind of non-local quasilinear (and possibly degenerate) parabolic equations. We first introduce the extended…

Probability · Mathematics 2019-08-21 Hanxiao Wang

This paper (alongside its companion, Part II \cite{BSDEYoung-II}) investigates backward stochastic differential equations (BSDEs) involving a nonlinear Young integral of the form $\int_{t}^{T}g(Y_{r})\eta(dr,X_{r})$, where the driver…

Probability · Mathematics 2025-08-01 Jian Song , Huilin Zhang , Kuan Zhang

We study a system of Forward-Backward Stochastic Differential Equations (FBSDEs) with time-delayed generators. The forward process includes a reflection component expressed via a Stieltjes integral, while the backward process takes the form…

Probability · Mathematics 2026-01-23 Luca Di Persio , Matteo Garbelli , Adrian Zalinescu

In this paper we propose a notion of viscosity solutions for path dependent semi-linear parabolic PDEs. This can also be viewed as viscosity solutions of non-Markovian backward SDEs, and thus extends the well-known nonlinear Feynman-Kac…

Analysis of PDEs · Mathematics 2014-01-15 Ibrahim Ekren , Christian Keller , Nizar Touzi , Jianfeng Zhang

We prove the existence of a $B$-continuous viscosity solution for a class of infinite dimensional semilinear partial differential equations (PDEs) using probabilistic methods. Our approach also yields a stochastic representation formula for…

Probability · Mathematics 2025-01-14 Lukas Wessels

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

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 investigate BSDEs where the driver contains a distributional term (in the sense of generalised functions) and derive general Feynman-Kac formulae related to these BSDEs. We introduce an integral operator to give sense to…

Probability · Mathematics 2019-07-18 Elena Issoglio , Francesco Russo

It is well-known since the work of Pardoux and Peng [12] that Backward Stochastic Differential Equations provide probabilistic formulae for the solution of (systems of) second order elliptic and parabolic equations, thus providing an…

Probability · Mathematics 2020-03-10 Etienne Pardoux , Aurel Rascanu

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

In this paper, we present a novel Feynman-Kac formula and investigate learning-based methods for approximating general nonlinear time-dependent Schr\"odinger equations which may be high-dimensional. Our formulation integrates both the…

Analysis of PDEs · Mathematics 2025-06-23 Hang Cheung , Jinniao Qiu , Yang Yang

We prove the existence of classical solutions to parabolic linear stochastic integro-differential equations with adapted coefficients using Feynman-Kac transformations, conditioning, and the interlacing of space-inverses of stochastic flows…

Probability · Mathematics 2014-11-27 James-Michael Leahy , Remigijus Mikulevicius
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