论文标题
基于机器学习的特征框架，用于非线性荡妇的参数表示
A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing
论文作者
论文摘要
对在容器内部创建液体宽度的参数表示的兴趣日益源于其在现代工程系统中的实际应用。另一方面，共振激发会引起不稳定和非线性水波，导致混乱的运动和非高斯信号。本文介绍了一个基于机器学习的新型框架，用于非线性液体宽道表示学习。提出的方法是基于顺序学习和稀疏正则化的参数建模技术。动力学分为两个部分：线性进化和非线性强迫。前者在嵌入式歧管上及时地进步，而后者则在时间演化中引起不同的行为，例如爆发和切换。通过在水平兴奋下，液体晃动的实验数据集在水平兴奋下，具有较宽的频率范围和各种垂直板条屏幕设置。
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant excitation, on the other hand, can cause unstable and nonlinear water waves, resulting in chaotic motions and non-Gaussian signals. This paper presents a novel machine learning-based framework for nonlinear liquid sloshing representation learning. The proposed method is a parametric modeling technique that is based on sequential learning and sparse regularization. The dynamics are categorized into two parts: linear evolution and nonlinear forcing. The former advances the dynamical system in time on an embedded manifold, while the latter causes divergent behaviors in temporal evolution, such as bursting and switching. The proposed framework's merit is demonstrated using an experimental dataset of liquid sloshing in a tank under horizontal excitation with a wide frequency range and various vertical slat screen settings.
