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A remarkable feature of fluid dynamics is its relationship with classical dynamics and statistical mechanics. This has motivated in the past mathematical investigations concerning, in a special way, the "derivation" based on kinetic theory,…

Developing efficient and stable approximations for high dimensional PDEs is of key importance for numerous applications. The language of Forward-Backward Stochastic Differential Equations (FBSDE), with its nonlinear Feynman-Kac formula,…

Numerical Analysis · Mathematics 2017-08-11 Arnaud Lionnet , Gonçalos dos Reis , Lukasz Szpruch

In this paper we study stochastic optimal control problems of general fully coupled forward-backward stochastic differential equations (FBSDEs). In Li and Wei [8] the authors studied two cases of diffusion coefficients $\sigma$ of FSDEs, in…

Probability · Mathematics 2012-06-26 Juan Li

In this paper, we use forward-backward stochastic differential systems to study the solution of two and d dimensional ($d\geq 3$) Navier-Stokes-$\alpha$ equation. For the two dimensional Navier-Stokes-$\alpha$ equation with space periodic…

Probability · Mathematics 2016-11-01 Guoping Liu

In the continuum flow regime, the Navier-Stokes equations are usually used for the description of gas dynamics. On the other hand, the Boltzmann equation is applied for the rarefied gas dynamics. Both equations are constructed from modeling…

Fluid Dynamics · Physics 2017-03-08 Kun Xu , Chang Liu

We present a computational alternative to probabilistic simulations for non-smooth stochastic dynamical systems that are prevalent in engineering mechanics. As examples, we target (1) stochastic elasto-plastic problems, which involve…

Probability · Mathematics 2019-05-23 Laurent Mertz , Georg Stadler , Jonathan Wylie

In this paper, we study forward-backward doubly stochastic differential equations driven by Brownian motions and Poisson process (FBDSDEP in short). Both the probabilistic interpretation for the solutions to a class of quasilinear…

Probability · Mathematics 2010-05-17 Qingfeng Zhu , Yufeng Shi

In this paper, we propose a novel data-driven framework for discovering probabilistic laws underlying the Feynman-Kac formula. Specifically, we introduce the first stochastic SINDy method formulated under the risk-neutral probability…

Mathematical Finance · Quantitative Finance 2025-11-13 Qi Feng , Guang Lin , Purav Matlia , Denny Serdarevic

This review provides a pedagogic and self-contained introduction to master equations and to their representation by path integrals. We discuss analytical and numerical methods for the solution of master equations, keeping our focus on…

Statistical Mechanics · Physics 2017-04-04 Markus F. Weber , Erwin Frey

We consider a stochastic Volterra integral equation with regular path-dependent coefficients and a Brownian motion as integrator in a multidimensional setting. Under an imposed absolute continuity condition, the unique solution is a…

Probability · Mathematics 2021-03-29 Alexander Kalinin

An optimal control problem is considered for a stochastic differential equation with the cost functional determined by a backward stochastic Volterra integral equation (BSVIE, for short). This kind of cost functional can cover the general…

Optimization and Control · Mathematics 2019-11-13 Hanxiao Wang , Jiongmin Yong

For stochastic systems with discrete time delay, the Fokker-Planck equation (FPE) of the one-time probability density function (PDF) does not provide a complete, self-contained probabilistic description. It explicitly involves the two-time…

Statistical Mechanics · Physics 2019-10-02 Sarah A. M. Loos , Sabine H. L. Klapp

This paper investigates solvability of fully coupled systems of forward-backward stochastic differential equations (FBSDEs) with irregular coefficients. In particular, we assume that the coefficients of the FBSDEs are merely measurable and…

Probability · Mathematics 2020-04-02 Peng Luo , Olivier Menoukeu-Pamen , Ludovic Tangpi

This paper investigates first the existence and uniqueness of solutions for McKean-Vlasov forward-backward doubly stochastic differential equations (MV-FBDSDEs) in infinite-dimensional real separable Hilbert spaces. These equations combine…

Probability · Mathematics 2024-07-15 AbdulRahman Al-Hussein , Abdelhakim Ninouh , Boulakhras Gherbal

In this paper we study time-inhomogeneous versions of one-dimensional Stochastic Differential Equations (SDE) involving the Local Time of the unknown process on curves. After proving existence and uniqueness for these SDE under mild…

Probability · Mathematics 2017-09-21 Pierre Etoré , Miguel Martinez

In this paper, we study a kind of optimal control problem for forward-backward stochastic differential equations (FBSDEs for short) of McKean--Vlasov type via the dynamic programming principle (DPP for short) motivated by studying the…

Optimization and Control · Mathematics 2024-07-09 Liangquan Zhang

The Fokker-Planck Equation (FPE) is a fundamental tool for the investigation of kinematic aspects of a wide range of systems. For systems governed by the non-additive entropy $S_q$, the Plastino-Plastino Equation (PPE) is the correct…

High Energy Physics - Phenomenology · Physics 2023-09-13 Eugenio Megias , Airton Deppman , Roman Pasechnik , Constantino Tsallis

We propose a natural, parameter-free, discrete-variable formulation of Feynman path integrals. We show that for discrete-variable quantum systems, Feynman path integrals take the form of walks on the graph whose weighted adjacency matrix is…

Quantum Physics · Physics 2025-12-08 Amir Kalev , Itay Hen

This paper establishes H\"{o}lder time regularity of solutions to coupled McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs). This is not only of fundamental mathematical interest, but also essential for their…

Probability · Mathematics 2020-11-16 Christoph Reisinger , Wolfgang Stockinger , Yufei Zhang

We present a discretization-free scalable framework for solving a large class of mass-conserving partial differential equations (PDEs), including the time-dependent Fokker-Planck equation and the Wasserstein gradient flow. The main…

Machine Learning · Computer Science 2023-11-15 Lingxiao Li , Samuel Hurault , Justin Solomon