  This work builds on the models and concepts presented in part 1 to learn
approximate dictionary representations of Koopman operators from data. Part I
of this paper presented a methodology for arguing the subspace invariance of a
Koopman dictionary. This methodology was demonstrated on the state-inclusive
logistic lifting (SILL) basis. This is an affine basis augmented with
conjunctive logistic functions. The SILL dictionary's nonlinear functions are
homogeneous, a norm in data-driven dictionary learning of Koopman operators. In
this paper, we discover that structured mixing of heterogeneous dictionary
functions drawn from different classes of nonlinear functions achieve the same
accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm.
We specifically show this by building a heterogeneous dictionary comprised of
SILL functions and conjunctive radial basis functions (RBFs). This mixed
dictionary achieves the same accuracy and dimensional scaling as deepDMD with
an order of magnitude reduction in parameters, while maintaining geometric
interpretability. These results strengthen the viability of dictionary-based
Koopman models to solving high-dimensional nonlinear learning problems.
