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The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called curse of dimensionality. Recent attempts rely on a combination…

Machine Learning · Computer Science 2023-07-31 Lorenz Richter , Leon Sallandt , Nikolas Nüsken

In this paper we study nonlinear partial differential equations (PDEs) that are used to model different value adjustments denoted generally as xVA. These adjustments are nowadays commonly added to the risk-free financial derivative values…

Analysis of PDEs · Mathematics 2023-07-03 Falko Baustian , Jan Pospíšil , Vladimír Švígler

A higher-order numerical method is presented for scalar valued, coupled forward-backward stochastic differential equations. Unlike most classical references, the forward component is not only discretized by an Euler-Maruyama approximation…

Numerical Analysis · Mathematics 2025-01-22 Balint Negyesi , Cornelis W. Oosterlee

In this manuscript, an original numerical procedure for the nonlinear peridynamics on arbitrarily--shaped two-dimensional (2D) closed manifolds is proposed. When dealing with non parameterized 2D manifolds at the discrete scale, the problem…

Numerical Analysis · Mathematics 2023-09-27 Alessandro Coclite , Giuseppe Maria Coclite , Francesco Maddalena , Tiziano Politi

This paper deals with the numerical approximation of semilinear parabolic stochastic partial differential equation (SPDE) driven simultaneously by Gaussian noise and Poisson random measure, more realistic in modeling real world phenomena.…

Numerical Analysis · Mathematics 2020-11-19 Jean Daniel Mukam , Antoine Tambue

This paper is dedicated to solving high-dimensional coupled FBSDEs with non-Lipschitz diffusion coefficients numerically. Under mild conditions, we provided a posterior estimate of the numerical solution that holds for any time duration.…

Probability · Mathematics 2022-01-19 Yifan Jiang , Jinfeng Li

Solving high-dimensional parabolic partial differential equations (PDEs) with deep learning methods is often computationally and memory intensive, primarily due to the need for automatic differentiation (AD) to compute large Hessian…

Numerical Analysis · Mathematics 2026-01-13 Wei Cai , Shuixin Fang , Tao Zhou

In this paper we wonder whether a quasilinear system of PDEs of first order admits Hamiltonian formulation with local and nonlocal operators. By using the theory of differential coverings, we find differential-geometric conditions necessary…

Mathematical Physics · Physics 2022-10-18 Pierandrea Vergallo

The porous medium equation (PME) is a typical nonlinear degenerate parabolic equation. An energetic variational approach has been studied in a recent work [6], in which the trajectory equation is obtained, and a few first order accurate…

Numerical Analysis · Mathematics 2020-06-23 Chenghua Duan , Wenbin Chen , Chun Liu , Cheng Wang , Xingye Yue

A new asymptotic expansion scheme for backward SDEs (BSDEs) is proposed.The perturbation parameter is introduced just to scale the forward stochastic variables within a BSDE. In contrast to the standard small-diffusion asymptotic expansion…

Computational Finance · Quantitative Finance 2014-12-23 Masaaki Fujii

High-dimensional partial differential equations (PDEs) pose significant challenges for numerical computation due to the curse of dimensionality, which limits the applicability of traditional mesh-based methods. Since 2017, the Deep BSDE…

Numerical Analysis · Mathematics 2025-05-26 Jiequn Han , Arnulf Jentzen , Weinan E

Partial differential equation (PDE) models with multiple temporal/spatial scales are prevalent in several disciplines such as physics, engineering, and many others. These models are of great practical importance but notoriously difficult to…

Numerical Analysis · Mathematics 2023-04-17 Junpeng Hu , Shi Jin , Lei Zhang

In this paper, we establish a general monotonicity formula of the following elliptic system $$ \Delta u_i+f_i(u_1,...,u_m)=0 \quad {\rm in} \Omega, \label{0.1} $$ where $\Omega\subset\subset \mathbb{R}^n$ is a bounded domain,…

Analysis of PDEs · Mathematics 2007-05-23 Li Ma , Xianfa Song , Lin Zhao

We introduce a Monte Carlo scheme for fully nonlinear parabolic nonlocal PDE's whose nonlinearity in of Hamilton-Jacobi-Bellman-Isaacs (HJBI for short). We avoid the difficulties of infinite L\'evy measure by truncation of the L\'evy…

Probability · Mathematics 2012-11-05 Arash Fahim

We obtain non-symmetric upper and lower bounds on the rate of convergence of general monotone approximation/numerical schemes for parabolic Hamilton Jacobi Bellman Equations by introducing a new notion of consistency. We apply our general…

Analysis of PDEs · Mathematics 2009-11-11 Guy Barles , Espen R. Jakobsen

This article revisits the approximation problem of systems of nonlinear delay differential equations (DDEs) by a set of ordinary differential equations (ODEs). We work in Hilbert spaces endowed with a natural inner product including a point…

Chaotic Dynamics · Physics 2015-09-11 Mickaël D. Chekroun , Michael Ghil , Honghu Liu , Shouhong Wang

High-dimensional parabolic partial integro-differential equations (PIDEs) appear in many applications in insurance and finance. Existing numerical methods suffer from the curse of dimensionality or provide solutions only for a given…

Numerical Analysis · Mathematics 2022-07-05 Rüdiger Frey , Verena Köck

Aim of this paper is to extend the continuous dependence estimates proved in \cite{JK1} to quasi-monotone systems of fully nonlinear second-order parabolic equations. As by-product of these estimates, we get an H\"older estimate for bounded…

Analysis of PDEs · Mathematics 2012-02-28 Fabio Camilli , Claudio Marchi

We construct and analyze a finite volume scheme for numerical solution of a three-dimensional Poisson equation. This is an extension of a two-dimensional approach by Suli 1991. Here we derive optimal convergence rates in the discrete H^1…

Numerical Analysis · Mathematics 2014-06-20 Mohammad Asadzadeh , Krzysztof Bartoszek

We propose a probabilistic numerical algorithm to solve Backward Stochastic Differential Equations (BSDEs) with nonnegative jumps, a class of BSDEs introduced in [9] for representing fully nonlinear HJB equations. In particular, this allows…

Probability · Mathematics 2019-07-11 Idris Kharroubi , Nicolas Langrené , Huyên Pham
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