Experimental Data

Data-assimilated model-informed reinforcement learning (DA-MIRL)

This paper introduces DA-MIRL, a novel framework that integrates sequential DA with off-policy RL to estimate and control high-dimensional, spatio-temporal chaos using only partial and noisy observations..

A real-time digital twin of azimuthal thermoacoustic instabilities

This paper develops novel methods to infer unknown unknowns in complex dynamical systems using data assimilation techniques.