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A learning based numerical method for Helmholtz equations with high frequency
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AI
DOI：10.1016/j.jcp.2024.113478.png)
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Abstract
En 中文
High-frequency issues have been remarkable challenges in numerical methods for partial differential equations. In this paper, a learning based numerical method (LbNM) is proposed for Helmholtz equation with high frequency. The main novelty is using Tikhonov regularization to stably learn the solution operator by utilizing relevant information especially the fundamental solutions. Then applying the solution operator to a new boundary input could quickly update the solution. Based on the method of fundamental solutions and the quantitative Runge approximation, we give the error estimate. This indicates interpretability and generalizability of the present method. Numerical results validate the error analysis and demonstrate the high- precision and high-efficiency features.
Keywords:
Learning based method
Helmholtz equation
High frequency
Fundamental solutions
Tikhonov regularization
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3.8
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1.6W
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