paper-with-me

Papers

Estimation of domain truncation error for a system of PDEs arising in option pricing

2024-01-28 · Anindya Goswami, Kuldip Singh Patel

In this paper, a multidimensional system of parabolic partial differential equations arising in European option pricing under a regime-switching market model is studied in details. For solving that numerically, one must truncate the domain and impose an artificial boundary data. By deriving an estimate of the domain truncation error at all the points in the truncated domain, we extend some results in the literature those deal with option pricing equation under constant regime case only. We differ from the existing approach to obtain the error estimate that is sharper in certain region of the domain. Hence, the minimum of proposed and existing gives a strictly sharper estimate. A comprehensive comparison with the existing literature is carried out by considering some numerical examples. Those examples confirm that the improvement in the error estimates is significant.

📄 PDF Abstract BibTeX arXiv:2401.15570

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning

2024-11-22 · Arvind Mohan, Ashesh Chattopadhyay, Jonah Miller

Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often called as NeuralPDEs, derived from first pr…

AMC26: High-performance DOb for robust position control

2026-01-05 · Emre Sariyildiz arxiv

This paper presents a new HPDOb that significantly improves disturbance estimation accuracy and robustness in motion control systems, surpassing the capabilities of conventional DObs. The proposed observer is analysed an…

Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs

2026-05-11 · Koichi Taniguchi, Sho Sonoda arxiv

Operator learning for partial differential equations (PDEs) aims to learn solution operators on infinite-dimensional function spaces from finite-resolution data. In this setting, it is important for the learned model to …

Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases

2024-10-04 · Madison Cooley, Varun Shankar, Robert M. Kirby, Shandian Zhe

Interest is rising in Physics-Informed Neural Networks (PINNs) as a mesh-free alternative to traditional numerical solvers for partial differential equations (PDEs). However, PINNs often struggle to learn high-frequency …

A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction

2019-07-01 · Sijing Li, Zhiwen Zhang, Hongkai Zhao

We propose a data-driven approach to solve multiscale elliptic PDEs with random coefficients based on the intrinsic low dimension structure of the underlying elliptic differential operators. Our method consists of offlin…

Dimensionality Reduction