Using machine learning with a genetic algorithm to concurrently optimize the electric machine power command and fuel flow acceleration schedule for an updated, higher fidelity version of the AGTF30 electrified gas turbine engine model.

Building off of previous work on Dynamic Components Analysis that explicitly maximizes the dynamic complexity in the data using predictive information, rather than variance like traditional PCA, to find subspaces of past neural data that have maximal mutual information with subspaces of future behavior data.