Projection frameworks for model reduction of weakly nonlinear systems

In this paper we present a generalization of popular linear model reduction methods, such as Lanczos- and Arnoldi-based algorithms based on rational approximation, to systems whose response to interesting external inputs can be described by a few terms in a functional series expansion such as a Volterra series. The approach allows automatic generation of macromodels that include frequency-dependent nonlinear effects.