Machine-learning inference of fluid variables from data using reservoir computing
Machine-learning inference of fluid variables from data using reservoir computing
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DOI：
10.1103/physreve.98.023111
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发表时间：
2018-08-31
影响因子：
2.4
通讯作者：
Saiki, Yoshitaka
中科院分区：
文献类型：
--
作者：
Nakai, Kengo;Saiki, Yoshitaka
利用油藏计算，我们推导了三维混沌流体流动的微观和宏观行为。在我们的推理过程中，我们不假定流体流动的物理过程的先验知识，只是假定它的行为是复杂的但确定的。我们给出了两种复杂行为的推理方法：第一种称为部分推理，需要在推理过程中连续了解部分时间序列数据和过去的时间序列数据；第二种称为完全推理，只需要过去的时间序列数据作为训练数据。对于第一种情况，我们能够推断微观流体变量的长时间运动。对于第二种情况，我们证明了仅由能量函数的过去数据构造的油藏动力学可以推断能量函数的未来行为并再现能谱。我们还表明，只需使用延迟坐标就可以从一次测量中推断出时间序列数据。这意味着，在不知道微观数据的情况下构造的油藏系统等价于描述能量函数宏观行为的动力系统。
We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the complex behavior: the first, called partial inference, requires continued knowledge of partial time-series data during the inference as well as past time-series data, while the second, called full inference, requires only past time-series data as training data. For the first case, we are able to infer long-time motion of microscopic fluid variables. For the second case, we show that the reservoir dynamics constructed from only past data of energy functions can infer the future behavior of energy functions and reproduce the energy spectrum. It is also shown that we can infer time-series data from only one measurement by using the delay coordinates. This implies that the obtained reservoir systems constructed without the knowledge of microscopic data are equivalent to the dynamical systems describing the macroscopic behavior of energy functions.