We are developing techniques for Consistency, Model Prediction, and Parameter Optimization using a dataset of experimental models, data, and uncertainty. Techniques include solution mapping, polynomial optimization, and semidefinite programming. Current work is on issues of dimensionality.

We are developing nonlinear state estimation strategies that will improve the performance of existing multiple model approaches to fault detection.