Reduced Order Modeling

Developed a novel, versatile, and rigorous methodology to learn non-Markovian closure parameterizations for low-fidelity models using data from high-fidelity simulations.

Investigation of a method for efficiently updating a reduced order model online, as new data becomes available corresponding to system changes.