A statistical perspective on nonlinear model reduction

We consider the advantages of statistically motivated reasoning in analyzing the nonlinear model reduction problem. By adopting an information-theoretic analysis, we argue that the general analog macromodeling is tractable on average by nonlinear reduction methods. We provide examples to illustrate the importance of utilizing prior information, and provide a general outline of algorithms based on reproducing kernel Hilbert space machinery.