ISAT is based on multiple linear regressions that are dynamically added as additional information is discovered.
ISAT maintains error control by defining finer granularity in regions of increased nonlinearity.
A binary tree search transverses cutting hyper-planes to locate a local linear approximation.
ISAT is an alternative to artificial neural networks that is receiving increased attention for desirable characteristics, namely: ISAT was first proposed by Stephen B. Pope for computational reduction of turbulent combustion simulation[1] and later extended to model predictive control.
[2] It has been generalized to an ISAT framework that operates based on any input and output data regardless of the application.