  The time-dependent fields obtained by solving partial differential equations
in two and more dimensions quickly overwhelm the analytical capabilities of the
human brain. A meaningful insight into the temporal behaviour can be obtained
by using scalar reductions, which, however, come with a loss of spatial detail.
Dynamic Mode Decomposition is a data-driven analysis method that solves this
problem by identifying oscillating spatial structures and their corresponding
frequencies. This paper presents the algorithm and provides a physical
interpretation of the results by applying the decomposition method to a series
of increasingly complex examples.
