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Reduced-order modelling and system identification can help us figure out theelementary degrees of freedom and the underlying mechanisms from thehigh-dimensional and nonlinear dynamics of fluid flow. Machine learning hasbrought new opportunities to these two processes and is revolutionisingtraditional methods. We show a framework to obtain a sparse human-interpretablemodel from complex high-dimensional data using machine learning and firstprinciples.

Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles