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A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing. (arXiv:2201.11663v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv framework learning machine machine learning representation