Inference and Prediction

An emerging challenge for inference and prediction of large-scale complex systems is to efficiently analyze and assimilate the ever-increasing high dimensional data produced by the vast number of engineered and natural systems.

The goals of our laboratory are to develop novel mathematical and statistical methods for

1. discovering fundamental structures and extracting useful information contained in high dimensional data; and

2. assimilating this information into the dynamical models in order to understand (and control) large-scale data-centric problems.

These new model-based and data-driven tools have a specific application to detect, control and mitigate dynamic instabilities in modern jet engines.