基于机器学习技术的波浪混沌系统分类与预测
Classification and prediction of wave chaotic systems with machine learning techniques
摘要：
复杂散射系统的波动特性与波长相比很大，并且在经典极限下表现出混沌，对系统细节非常敏感。对于一个特定的构形，波动方程的解在小的扰动下会有很大的变化。由于这种极端的灵敏度，很难从作为能量或频率的函数的散射数据中简单地辨别出关于复杂系统的基本信息，至少用眼睛是这样。在这项工作中，我们采用监督机器学习算法来揭示和分类数据中呈现的复杂散射系统的隐藏信息。作为一个例子，我们能够区分连接的空腔在一个线性链的弱耦合损耗外壳从测量的反射数据的总数。预测机器学习算法的扰动复杂散射系统的未来状态也训练与递归神经网络。给定有限的训练数据序列，该算法可以预测的反射/透射性能。
英文摘要：
The wave properties of complex scattering systems that are large compared to the wavelength, and show chaos in the classical limit, are extremely sensitive to system details. A solution to the wave equation for a specific configuration can change substantially under small perturbations. Due to this extreme sensitivity, it is difficult to discern basic information about a complex system simply from scattering data as a function of energy or frequency, at least by eye. In this work, we employ supervised machine learning algorithms to reveal and classify hidden information about the complex scattering system presented in the data. As an example we are able to distinguish the total number of connected cavities in a linear chain of weakly coupled lossy enclosures from measured reflection data. A predictive machine learning algorithm for the future states of a perturbed complex scattering system is also trained with a recurrent neural network. Given a finite training data series, the reflection/transmission properties can be forecast by the proposed algorithm.
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