论文标题
涡流计算：桥接流体动力学及其信息处理能力
Computing with vortices: Bridging fluid dynamics and its information-processing capability
论文作者
论文摘要
在此，Karman Vortex系统被认为是一个大型的复发性神经网络，并且通过模拟非线性动力学系统和内存容量来评估计算能力。因此，揭示了Karman Vortex系统计算性能的雷诺数依赖性，并且在Karman Vortex脱落开始时，在关键的雷诺数附近实现了最佳计算性能，这与Hopf Bifurcation相关。我们的发现提高了人们对流体动力学物理特性及其计算能力之间的关系的理解，并为广泛相信的观点提供了一种替代方案，即信息处理能力在混乱的边缘变得最佳。
Herein, the Karman vortex system is considered to be a large recurrent neural network, and the computational capability is numerically evaluated by emulating nonlinear dynamical systems and the memory capacity. Therefore, the Reynolds number dependence of the Karman vortex system computational performance is revealed and the optimal computational performance is achieved near the critical Reynolds number at the onset of Karman vortex shedding, which is associated with a Hopf bifurcation. Our finding advances the understanding of the relationship between the physical properties of fluid dynamics and its computational capability as well as provides an alternative to the widely believed viewpoint that the information processing capability becomes optimal at the edge of chaos.
