Accelerating large-scale non-linear models for monitoring and control using spatial and temporal correlations

For non-linear state space models, model reduction alone does not decrease the time required to compute the state update. This paper suggests methods for generating models that approximate the original reduced order models by faster equivalents. Where updating reduced order models normally requires the computation of the original large-scale model, we only compute the original large-scale model for a subset of its states. The new state of the reduced order model can then no longer be computed exactly, but has to be estimated from subset of states that have been computed using the large-scale model. It is shown that the new state of the reduced order model can be estimated accurately using spatial and temporal correlations. This acceleration method can be viewed as a partial linearization of the system equations. The methods in this paper are illustrated using a simulation example of a physical system.