论文标题
非线性动力学的转移学习及其在流体湍流中的应用
Transfer learning for nonlinear dynamics and its application to fluid turbulence
论文作者
论文摘要
我们引入了非线性动力学的传输学习，该学习可以通过使用少量数据来有效地预测混乱动力学。对于洛伦兹混乱，通过优化传输速率，我们比传统方法通过数量级来实现更准确的推断。此外，令人惊讶的是，少量的学习足以推断纳维尔 - 斯托克斯湍流的能量耗散率，因为由于湍流的小规模普遍性，我们可以转移大量从较低雷诺数的湍流数据中学到的知识。
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.
