Figure 14.

The application of the diffusion map algorithm to the Chung-Lu coupled Kuramoto model with an output-only-informed kernel yields a single significant diffusion map coordinate, ϕ₁. (A) Coloring the parameter space (ω, κ) with this new coordinate reveals the relationship between the original parameters and the “effective” diffusion map parameter. (B) Level sets of the significant diffusion map coordinate ϕ₁ in the original parameter space (ω, κ). These level sets were found by means of the marching squares algorithm (Maple, 2003). A functional form of the mapping between parameters and the diffusion map coordinate could be found with typical regression techniques or by using machine learning techniques like neural networks. (C) Coloring the parameter space with the steady state phases produces a coloring similar to the one in (A) as there is a one-to-one map between θ and ϕ₁.