arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.20719math.NAcs.NA
收藏

高频率亥姆霍兹方程的自适应求解:多级深度学习

The Adaptive Solution of High-Frequency Helmholtz Equations via Multi-Grade Deep Learning

Jilin University(吉林大学);Shandong Normal University(山东师范大学);Old Dominion University(老道明大学)

Peiyao Zhao, Rui Wang, Tingting Wu, Yuesheng Xu

AI总结:

本文提出FD-MGDL框架,结合有限差分与多级深度学习,高效求解高频亥姆霍兹方程,显著优于传统方法。

摘要:

亥姆霍兹方程在声学、电磁学和地震成像中是基础,但高频领域因“污染效应”而极具挑战性。我们提出FD-MGDL,一种结合有限差分方案与多级深度学习的自适应框架,以高效求解高频解。尽管传统PINNs在频谱偏差和自动微分开销方面存在困难,FD-MGDL采用渐进训练策略,逐步增加隐藏层以细化解并保持稳定性。关键在于使用ReLU激活时,我们的算法将高度非凸训练问题转化为一系列凸子问题。在2D和3D中使用波数高达κ=200的数值实验表明,FD-MGDL在准确性和速度上显著优于单级和传统神经求解器。应用于非均匀凹速度模型时,该框架能准确解析波聚焦和光路,超越五点有限差分法在捕捉尖锐相变和振幅尖峰方面的能力。这些结果确立了FD-MGDL作为复杂领域中高频波动方程的稳健、可扩展求解器。

英文摘要:

The Helmholtz equation is fundamental for modeling wave propagation in acoustics, electromagnetics, and geophysics; however, high-frequency regimes remain notoriously difficult due to the severe numerical ``pollution effect.'' We propose FD-MGDL, an adaptive framework that synergizes finite difference discretizations with Multi-Grade Deep Learning (MGDL) to efficiently resolve high-frequency wavefields. Unlike standard physics-informed neural networks (PINNs), which frequently suffer from spectral bias and heavy automatic differentiation overhead, FD-MGDL employs a progressive, grade-wise training strategy that incrementally incorporates shallow sub-networks to refine residual errors. By leveraging ReLU activations in refinement grades, the framework reformulates the highly non-convex global optimization problem into a sequence of tractable convex subproblems, dramatically improving training stability and convergence reliability. Extensive numerical experiments in two and three dimensions with wavenumbers up to $κ=200$ demonstrate that FD-MGDL significantly outperforms single-grade networks and standard benchmark neural solvers in both accuracy and computational efficiency. When applied to an inhomogeneous concave velocity model, the proposed method accurately captures wave focusing and caustic formations, markedly outperforming classical five-point finite difference schemes in resolving sharp phase transitions and peak amplitudes. These results establish FD-MGDL as a robust, scalable, and mathematically grounded paradigm for high-frequency wave simulation in complex media.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑