Scientific ML

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

Implementation of Machine Learning algorithms for solving nonlinear partial differential equations, in a hybrid framework.