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arXiv 2601.12907math.NAcs.NA
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用于高频微分方程的机器学习

Machine Learning for highly oscillatory differential equations

IRMAR, Université de Rennes(雷恩大学 IRMAR)

Maxime Bouchereau

AI总结:

本文提出利用神经网络和微宏技术,高效求解高频微分方程,替代传统预计算方法。

摘要:

高频微分方程在多尺度问题中普遍存在,通常过于复杂无法解析求解。然而,已开发出几种数值方法来近似其解。尽管这些方法已显示出其效率,但策略的第一部分通常涉及从平均理论中进行大量预计算。在本文中,我们利用神经网络(机器学习)来近似第一部分中所需的向量场,并结合微宏技术,高效地解决振荡问题。我们通过数值模拟来说明我们的工作。

英文摘要:

Highly oscillatory differential equations, commonly encountered in multi-scale problems, are often too complex to solve analytically. However, several numerical methods have been developed to approximate their solutions. Although these methods have shown their efficiency, the first part of the strategy often involves heavy pre-computations from averaging theory. In this paper, we leverage neural networks (machine learning) to approximate the vector fields required by the pre-computations in the first part, and combine this with micro-macro techniques to efficiently solve the oscillatory problem. We illustrate our work by numerical simulations.

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。

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