论文标题
部分可观测时空混沌系统的无模型预测
Fluctuation-driven thermal transport in graphene double-layers at charge neutrality
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。
We develop a theory of fluctuation-driven phenomena in thermal transport in graphene double-layers. We work in the regime of electron hydrodynamics and focus on the double charge neutrality point. Although at the neutrality point charge transport is decoupled from the hydrodynamic flow, thermal fluctuations of electron density cause both drag and heat transfer between the layers. The thermal transport in the bilayer system is governed by these two phenomena. We express the drag friction coefficient and the interlayer thermal conductivity in terms of the interlayer distance and the intrinsic conductivity of the electron liquid. We then obtain the thermal conductance matrix and determine the spatial dependence of the hydrodynamic velocity and temperature in the system. For shorter system the thermal drag resistance is determined by drag. In longer systems the situation of perfect thermal drag is realized, in which the hydrodynamic velocities in both layers become equal in the interior of the systems. Estimates are given for the monolayer and bilayer graphene devices. The predictions of our theory can be tested by the high-resolution thermal imaging and Johnson-Nyquist nonlocal noise thermometry.
