Boundary Corrections for Kernel Approximation to Differential Operators

Abstract The 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 computational cost as an explicit finite difference scheme but can exhibit order reduction at boundaries. In previous work on periodic domains, [9, 10], order reduction was addressed, yielding high-order accuracy. The issue addressed in this work is the elimination of order reduction of the kernel-based approach for a more general set of boundary conditions. Further, we consider the case of both first and second order operators. To demonstrate the theory, we provide not only the mathematical proofs but also experimental results by applying various boundary conditions to different types of equations. The results agree with the theory, demonstrating a systematic path to high order for kernel-based methods on bounded domains.

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Publication Details

Journal
Journal of Scientific Computing
Published
2025-11-16
DOI
https://doi.org/10.1007/s10915-025-03083-w
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Boundary Corrections for Kernel Approximation to Differential Operators

Hyoseon Yang, Sining Gong, Andrew Christlieb
Journal of Scientific Computing
Model Reduction and Neural Networks
article

Boundary Corrections for Kernel Approximation to Differential Operators

Hyoseon Yang, Sining Gong, Andrew Christlieb
article en

Abstract

Abstract The 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 computational cost as an explicit finite difference scheme but can exhibit order reduction at boundaries. In previous work on periodic domains, [9, 10], order reduction was addressed, yielding high-order accuracy. The issue addressed in this work is the elimination of order reduction of the kernel-based approach for a more general set of boundary conditions. Further, we consider the case of both first and second order operators. To demonstrate the theory, we provide not only the mathematical proofs but also experimental results by applying various boundary conditions to different types of equations. The results agree with the theory, demonstrating a systematic path to high order for kernel-based methods on bounded domains.

Journal of Scientific ComputingVol. 105(3)
Kyung Hee University (KR), Michigan State University (US)
National Science Foundation, U.S. Department of Energy, National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Office of Naval Research, Air Force Office of Scientific Research
Openalex Percentile: Top 72%
Model Reduction and Neural Networks
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