A dimensionality reducing model for distributed filtering.

Abstract

An approach is made to filtering of two-dimensional steady-state problems based on the notion of nearest neighbor interaction, i.e., at a given point in the spatial grid, the value at the variable of interest can be assumed to depend only on the values at adjacent grid points. It is shown that for linear steady-state problems significant dimensionality reductions can be accomplished. It was possible to achieve the desired results using a small amount of computer time and without getting into stability difficulties.