Reduced Order Modeling
Neural Closure Models for Dynamical Systems
Developed a novel, versatile, and rigorous methodology to learn non-Markovian closure parameterizations for low-fidelity models using data from high-fidelity simulations.
Active rank-1 updates to POD based reduced order models
Investigation of a method for efficiently updating a reduced order model online, as new data becomes available corresponding to system changes.