流量(数学)
代表(政治)
可解释性
领域(数学)
计算机科学
特征(语言学)
情态动词
算法
分解
特征提取
交货地点
数学
模式识别(心理学)
人工智能
几何学
生态学
农学
语言学
哲学
化学
政治
政治学
高分子化学
纯数学
法学
生物
作者
Zihao Wang,Guiyong Zhang,Huakun Huang,Hao Xu,Tiezhi Sun
标识
DOI:10.1016/j.oceaneng.2023.116003
摘要
Principal Orthogonal Decomposition (POD), as a data-driven method for extracting key features from fluid flow, overlooks the potential interactions and correlations among variables. This limitation restricts its effectiveness in capturing the underlying physical characteristics of the system. In this paper, we draw inspiration from the concept of joint representation in the field of multi-modal learning, and propose the use of Joint POD (JPOD) as a promising fluid mechanics analysis tool to extract multi-variable features of cavitation flow. We elaborate on the differences between JPOD and POD in four aspects: reconstruction error, data structure, flow field features, and flow modalities. JPOD increases the reconstruction error slightly but strengthens the correlations between multi-variables in the flow field. The modalities obtained by JPOD exhibit strong regularity and interpretability, and the pattern features between different variables are related, which cannot be achieved by the POD algorithm alone. The successful combination of joint representation and POD algorithm demonstrates that it can be regarded as an optimization and standardization method for traditional modal decomposition algorithms, with broad application prospects in the field of modal decomposition.
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