  Reduced-order modelling and system identification can help us figure out the
elementary degrees of freedom and the underlying mechanisms from the
high-dimensional and nonlinear dynamics of fluid flow. Machine learning has
brought new opportunities to these two processes and is revolutionising
traditional methods. We show a framework to obtain a sparse human-interpretable
model from complex high-dimensional data using machine learning and first
principles.
