arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2009.01407physics.flu-dynmath.DSnlin.CDphysics.comp-phstat.ML
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非线性动力学的迁移学习及其在流体turbulence中的应用

Transfer learning for nonlinear dynamics and its application to fluid turbulence

Masanobu Inubushi, Susumu Goto

摘要:

我们引入了非线性动力学的迁移学习,它可以通过利用少量数据来有效地预测混沌动力学。对于Lorenz混沌,通过优化传输速率,我们实现了比传统方法高一个数量级的精确推理。此外,令人惊讶的少量学习就足以推断出Navier-Stokes湍流的能量耗散率,因为由于湍流的小尺度普遍性,我们可以在较低的雷诺数下转移从湍流数据中学到的大量知识。

英文摘要:

We introduce transfer learning for nonlinear dynamics, which enables efficient predictions of chaotic dynamics by utilizing a small amount of data. For the Lorenz chaos, by optimizing the transfer rate, we accomplish more accurate inference than the conventional method by an order of magnitude. Moreover, a surprisingly small amount of learning is enough to infer the energy dissipation rate of the Navier-Stokes turbulence because we can, thanks to the small-scale universality of turbulence, transfer a large amount of the knowledge learned from turbulence data at lower Reynolds number.

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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