  Nonlinear dynamical systems with symmetries exhibit a rich variety of
behaviors, including complex attractor-basin portraits and enhanced and
suppressed bifurcations. Symmetry arguments provide a way to study these
collective behaviors and to simplify their analysis. The Koopman operator is an
infinite dimensional linear operator that fully captures a system's nonlinear
dynamics through the linear evolution of functions of the state space.
Importantly, in contrast with local linearization, it preserves a system's
global nonlinear features. We demonstrate how the presence of symmetries
affects the Koopman operator structure and its spectral properties. In fact, we
show that symmetry considerations can also simplify finding the Koopman
operator approximations using the extended and kernel dynamic mode
decomposition methods (EDMD and kernel DMD). Specifically, representation
theory allows us to demonstrate that an isotypic component basis induces block
diagonal structure in operator approximations, revealing hidden organization.
Practically, if the data is symmetric, the EDMD and kernel DMD methods can be
modified to give more efficient computation of the Koopman operator
approximation and its eigenvalues, eigenfunctions, and eigenmodes. Rounding out
the development, we discuss the effect of measurement noise.
