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Sparse Sensing and Modal Decomposition for Unsteady Fluid Flows
This work explores data-driven methods, including sparse sampling, modal decomposition and machine learning techniques, for high-dimensional systems in fluid dynamics. Fluid flows are characterized by their nonlinearity, multi-scale structures and unsteady behaviors. Understanding the patterns and their evolving dynamics is ...