核心概念
The author presents a methodology to decompose flows with multiple transports using shifted proper orthogonal decomposition, optimizing co-moving fields directly and penalizing their nuclear norm. This approach enhances separation and accuracy in describing transport phenomena.
摘要
The content introduces a novel method for decomposing flows with multiple transports, extending the shifted proper orthogonal decomposition. By optimizing co-moving fields directly and penalizing their nuclear norm, the methodology improves separation and accuracy in describing transport phenomena. The study includes numerical comparisons against existing methods on synthetic data benchmarks and showcases the separation ability of the proposed methods on various flow scenarios.
统计
We report a numerical comparison with existing methods against synthetic data benchmarks.
The resulting methodology is the basis of a new analysis paradigm that results in the same interpretability as the POD for individual co-moving fields.
Leveraging tools from convex optimization, three proximal algorithms are derived to solve the decomposition problem.