Recent Publications

Real-time forecasting of chaotic dynamics from sparse data and autoencoders

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..

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.

Inferring unknown unknowns

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