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We propose a new, unified approach to solving jump-diffusion partial integro-differential equations (PIDEs) that often appear in mathematical finance. Our method consists of the following steps. First, a second-order operator splitting on…

Computational Finance · Quantitative Finance 2014-04-15 Andrey Itkin

Integro-partial differential equations occur in many contexts in mathematical physics. Typical examples include time-dependent diffusion equations containing a parameter (e.g., the temperature) that depends on integrals of the unknown…

Analysis of PDEs · Mathematics 2007-05-23 Peter A. Becker

The reaction-diffusion model can generate a wide variety of spatial patterns, which has been widely applied in chemistry, biology, and physics, even used to explain self-regulated pattern formation in the developing animal embryo. In this…

Numerical Analysis · Mathematics 2020-01-29 Hui Zhang , Xiaoyun Jiang , Fanhai Zeng , George Em Karniadakis

The obstacle problem is a class of free boundary problems which finds applications in many disciplines such as porous media, financial mathematics and optimal control. In this paper, we propose two operator-splitting methods to solve the…

Numerical Analysis · Mathematics 2023-02-08 Hao Liu , Dong Wang

This chapter presents some numerical methods to solve problems in the fractional calculus of variations and fractional optimal control. Although there are plenty of methods available in the literature, we concentrate mainly on approximating…

Optimization and Control · Mathematics 2014-05-19 Shakoor Pooseh , Ricardo Almeida , Delfim F. M. Torres

Kernel-based approach to operator approximation for partial differential equations has been shown to be unconditionally stable for linear PDEs and numerically exhibit unconditional stability for non-linear PDEs. These methods have the same…

Numerical Analysis · Mathematics 2025-11-25 Andrew Christlieb , Sining Gong , Hyoseon Yang

This paper proposes a new second-order symmetric algorithm for solving decoupled forward-backward stochastic differential equations. Inspired by the alternating direction implicit splitting method for partial differential equations, we…

Numerical Analysis · Mathematics 2026-01-16 Wenbo Wang , Guangyan Jia

We propose a solution for linear inverse problems based on higher-order Langevin diffusion. More precisely, we propose pre-conditioned second-order and third-order Langevin dynamics that provably sample from the posterior distribution of…

Machine Learning · Statistics 2023-12-08 Nicolas Zilberstein , Ashutosh Sabharwal , Santiago Segarra

We present new high-order Alternating Direction Implicit (ADI) schemes for the numerical solution of initial-boundary value problems for convection-diffusion equations with mixed derivative terms. Our approach is based on the…

Numerical Analysis · Mathematics 2015-05-29 Bertram Düring , Michel Fournié , Alain Rigal

Partial differential equations (PDEs) involving fractional Laplace operators have been increasingly used to model non-local diffusion processes and are actively investigated using both analytical and numerical approaches. The purpose of…

Analysis of PDEs · Mathematics 2021-03-02 Noémie Ehstand , Christian Kuehn , Cinzia Soresina

Reduced-order modeling is an efficient approach for solving parameterized discrete partial differential equations when the solution is needed at many parameter values. An offline step approximates the solution space and an online step…

Numerical Analysis · Mathematics 2017-04-05 Howard C. Elman , Virginia Forstall

We analyze operator splitting methods applied to scalar equations with a nonlinear advection operator, and a linear (local or nonlocal) diffusion operator or a linear dispersion operator. The advection velocity is determined from the scalar…

Numerical Analysis · Mathematics 2012-02-02 Helge Holden , Kenneth H. Karlsen , Trygve K. Karper

Over the past few decades, there has been substantial interest in evolution equations that involving a fractional-order derivative of order $\alpha\in(0,1)$ in time, due to their many successful applications in engineering, physics, biology…

Numerical Analysis · Mathematics 2019-01-30 Bangti Jin , Raytcho Lazarov , Zhi Zhou

We extend to the N-level Bloch model the splitting scheme which use exact numerical solutions of sub-equations. These exact solutions involve matrix exponentials which we want to avoid to calculate at each time step. We use Newton…

Numerical Analysis · Mathematics 2019-09-25 Marc Songolo , Brigitte Bidégaray-Fesquet

In this paper, we propose and analyze a positivity-preserving, energy stable numerical scheme for certain type reaction-diffusion systems involving the Law of Mass Action with the detailed balance condition. The numerical scheme is…

Numerical Analysis · Mathematics 2021-04-07 Chun Liu , Cheng Wang , Yiwei Wang

Since their introduction in 1967, Lawson methods have achieved constant interest in the time discretization of evolution equations. The methods were originally devised for the numerical solution of stiff differential equations. Meanwhile,…

Numerical Analysis · Mathematics 2018-06-19 Marlis Hochbruck , Alexander Ostermann

We prove sharp estimates for the decay in time of solutions to a rather general class of non-local in time subdiffusion equations on a bounded domain subject to a homogeneous Dirichlet boundary condition. Important special cases are the…

Analysis of PDEs · Mathematics 2013-10-02 Vicente Vergara , Rico Zacher

Can graded meshes yield more accurate numerical solution than uniform meshes? A time-dependent nonlocal diffusion problem with a weakly singular kernel is considered using collocation method. For its steady-state counterpart, under the…

Numerical Analysis · Mathematics 2024-02-01 Minghua Chen , Chao Min , Jiankang Shi , Jizeng Wang

The diffusion system with time-fractional order derivative is of great importance mathematically due to the nonlocal property of the fractional order derivative, which can be applied to model the physical phenomena with memory effects. We…

Analysis of PDEs · Mathematics 2021-03-24 Mengmeng Zhang , Jijun Liu

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the discrepancy between discrete data and continuous modeling.…

Machine Learning · Computer Science 2024-10-31 Yuxuan Gu , Xiaocheng Feng , Lei Huang , Yingsheng Wu , Zekun Zhou , Weihong Zhong , Kun Zhu , Bing Qin
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