Machine learning models to extract accurate material transport

The student will develop machine learning models to extract accurate material transport and strength coefficients from time-series data, obtained from high-fidelity simulations as well as experiments with high-power lasers. These will be used to improve our understanding of materials under extreme conditions, particularly in presence of non-local processes involving turbulent and magnetized plasmas. The goal of the project is to develop a novel graph neural network framework to address the complex micro-physics of material properties and to identify their emergent behaviour via closed mathematical expressions using symbolic regression techniques. This has practical applications ranging from the design of spacecraft components to the modelling of the energy balance in cluster of galaxies.