论文标题
部分可观测时空混沌系统的无模型预测
Thermal spin injection from a ferromagnet into graphene by transverse and longitudinal current
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Graphene is a very promising material in spintronics due to both its high electric mobility and low intrinsic spin-obit coupling. Electronic spins can be injected from a ferromagnetic material through a tunnel contact into graphene owing to a spin relaxation length as high as 5μm. In recent years, a new approach creating spin current employed thermal effects and heat flow. Here, by applying transverse and longitudinal current to a grahene spin valve device, the interplay between the heat spin current and the charge spin current is investigated. The non-local spin voltage is enhanced by the thermal spin injection and the thermal spin voltage reaches a maximum close to the Dirac point which makes graphene a promising material for a future thermoelectric spin device due to its long spin lifetime and spin diffusion length.
