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In this paper we propose a new numerical method for solving stochastic differential equations (SDEs). As an application of this method we propose an explicit numerical scheme for a super linear SDE for which the usual Euler scheme diverges.

Numerical Analysis · Mathematics 2013-03-14 Nikolaos Halidias

The full history recursive multilevel Picard approximation method for semilinear parabolic partial differential equations (PDEs) is the only method which provably overcomes the curse of dimensionality for general time horizons if the…

Numerical Analysis · Mathematics 2022-04-29 Martin Hutzenthaler , Tuan Anh Nguyen

We consider some certain nonlinear perturbations of the stochastic linear-quadratic optimization problems and study the connections between their solutions and the corresponding Markovian backward stochastic diferential equations (BSDEs).…

Optimization and Control · Mathematics 2013-01-01 Coskun Cetin

In this paper we consider multi-dimensional partial differential equations of parabolic type involving divergence form operators that possess a discontinuous coefficient matrix along some smooth interface. The solution of the equation is…

Probability · Mathematics 2020-03-27 Pierre Etore , Miguel Martinez

Recent advances in deep learning makes solving parabolic partial differential equations (PDEs) in high dimensional spaces possible via forward-backward stochastic differential equation (FBSDE) formulations. The implementation of most…

Numerical Analysis · Mathematics 2025-06-19 Wenjun Xu , Wenzhong Zhang

In this paper, we propose a new kind of numerical scheme for high-dimensional backward stochastic differential equations based on modified multi-level Picard iteration. The proposed scheme is very similar to the original multi-level Picard…

Numerical Analysis · Mathematics 2019-05-06 Chol-Kyu Pak , Mun-Chol Kim , Hun O

To study the nonlinear properties of complex natural phenomena, the evolution of the quantity of interest can be often represented by systems of coupled nonlinear stochastic differential equations (SDEs). These SDEs typically contain…

Optimization and Control · Mathematics 2024-10-22 Jan Bartsch , Robert Denk , Stefan Volkwein

Inspired by recent progress in quantum algorithms for ordinary and partial differential equations, we study quantum algorithms for stochastic differential equations (SDEs). Firstly we provide a quantum algorithm that gives a quadratic…

Quantum Physics · Physics 2021-06-30 Dong An , Noah Linden , Jin-Peng Liu , Ashley Montanaro , Changpeng Shao , Jiasu Wang

In this work, we investigate the numerical approximation of the second order non-autonomous semilnear parabolic partial differential equation (PDE) using the finite element method. To the best of our knowledge, only the linear case is…

Numerical Analysis · Mathematics 2020-01-27 Antoine Tambue , Jean Daniel Mukam

H\"older estimates for second derivatives are proved for solutions of fully nonlinear parabolic equations in two space variables. Related techniques extend the regularity theory for fully nonlinear parabolic equations in higher dimensions.

Analysis of PDEs · Mathematics 2007-05-23 Ben Andrews

We analyze a class of nonlinear partial differential equations (PDEs) defined on $\mathbb{R}^d \times \mathcal{P}_2(\mathbb{R}^d),$ where $\mathcal{P}_2(\mathbb{R}^d)$ is the Wasserstein space of probability measures on $\mathbb{R}^d$ with…

Probability · Mathematics 2015-04-23 Jean-François Chassagneux , Dan Crisan , François Delarue

This article deals with the numerical resolution of backward stochastic differential equations. Firstly, we consider a rather general case where the filtration is generated by a Brownian motion and a Poisson random measure. We provide a…

Probability · Mathematics 2008-12-18 Emmanuel Gobet , Jean-Philippe Lemor

In this work, we study the numerical approximation of a class of singular fully coupled forward backward stochastic differential equations. These equations have a degenerate forward component and non-smooth terminal condition. They are…

Numerical Analysis · Mathematics 2022-08-17 Jean-François Chassagneux , Mohan Yang

A splitting scheme for backward doubly stochastic differential equations is proposed. The main idea is to decompose a backward doubly stochastic differential equation into a backward stochastic differential equation and a stochastic…

Numerical Analysis · Mathematics 2021-03-17 Feng Bao , Yanzhao Cao , He Zhang

We study the Cauchy problem for fully nonlinear (stochastic) parabolic partial differential equations. We provide both in deterministic and stochastic case the existence of a maximal defined solution for the problem and we provide suitable…

Analysis of PDEs · Mathematics 2018-04-12 Antonio Agresti

In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations (BSDEs), and we analyze the convergence of the proposed method. The…

Numerical Analysis · Mathematics 2024-08-01 E. Gobet , J. G. López-Salas , C. Vázquez

Modeling physical phenomena like heat transport and diffusion is crucially dependent on the numerical solution of partial differential equations (PDEs). A PDE solver finds the solution given coefficients and a boundary condition, whereas an…

Graphics · Computer Science 2022-08-04 Ekrem Fatih Yılmazer , Delio Vicini , Wenzel Jakob

This paper concerns with numerical approximations of solutions of second order fully nonlinear partial differential equations (PDEs). A new notion of weak solutions, called moment solutions, is introduced for second order fully nonlinear…

Numerical Analysis · Mathematics 2007-08-14 Xiaobing Feng , Michael Neilan

We extend the wellposedness results for second order backward stochastic differential equations introduced by Soner, Touzi and Zhang \cite{stz} to the case of a bounded terminal condition and a generator with quadratic growth in the $z$…

Probability · Mathematics 2014-04-14 Dylan Possamaï , Chao Zhou

In this paper, we study the backward stochastic differential equation (BSDE) with two nonlinear mean reflections, which means that the constraints are imposed on the distribution of the solution but not on its paths. Based on the backward…

Probability · Mathematics 2023-07-13 Hanwu Li