An approximation for solutions to Helmholtz equation in polygons and its application in learning based numerical method
Abstract
A learning based method for Helmholtz equations was proposed in [J. Comput. Phys. 520:113478,2025]. Based on Runge's approximation, it can be proved that when the solution can be continued outside the computational domain, using fundamental solutions as learning samples is sufficient to obtain the solution mapping with spectral accuracy. However, for complex geometries, such as domains with corners, approximation by fundamental solutions may only have logarithmic convergence due to the regularity issue, which is computationally impracticable. Moreover, approximation of a solution to the elliptic equation with those on larger domains is an ill-posed problem. In this work, we investigate how to feed additional learning samples to essentially get a practicable convergence rate and a stable algorithm, so as to ensure the generalizability of the learning based method. Error analysis and numerical examples illustrate flexibility and high-accuracy features.
Related
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- On the Existence, Uniqueness, and Numerical Solutions of a Time-Fractional Pseudo-Parabolic Equation Under Periodic Boundary Conditions
- Cartesian PML truncation for the Helmholtz equation is exponentially accurate at high frequency
BibTeX
@misc{chen2026approximation,
title = {{An approximation for solutions to Helmholtz equation in polygons and its application in learning based numerical method}},
author = {Yu Chen and Jin Cheng},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.12506},
eprint = {2611.12506},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.12506},
}
RIS
TY - UNPB
TI - An approximation for solutions to Helmholtz equation in polygons and its application in learning based numerical method
AU - Yu Chen
AU - Jin Cheng
PY - 2026
DA - 2026/11/09
PB - arXiv
DO - 10.48550/arxiv.2611.12506
UR - https://arxiv.org/abs/2611.12506
AB - A learning based method for Helmholtz equations was proposed in [J. Comput. Phys. 520:113478,2025]. Based on Runge's approximation, it can be proved that when the solution can be continued outside the computational domain, using fundamental solutions as learning samples is sufficient to obtain the solution mapping with spectral accuracy. However, for complex geometries, such as domains with corners, approximation by fundamental solutions may only have logarithmic convergence due to the regularity issue, which is computationally impracticable. Moreover, approximation of a solution to the elliptic equation with those on larger domains is an ill-posed problem. In this work, we investigate how to feed additional learning samples to essentially get a practicable convergence rate and a stable algorithm, so as to ensure the generalizability of the learning based method. Error analysis and numerical examples illustrate flexibility and high-accuracy features.
ER -