A numerical projection-based approach to nonlinear model reduction and identification

We propose a general method for nonlinear chemical/biochemical model reduction and identification, inspired by the concept of subspace identification. We propose to use artificial neural networks to find a nonlinear projection operator that serves to define the reduced state out of the full state or out of an input-output time series. We investigate the viability of the method for both deterministic and stochastic systems.