We are always looking for motivated students that are eager to work on topics related to solid mechanics and its intersection with machine learning and data-driven methods. You can find a list of available projects below, while we are also open to suggestions of project topics.
Granular materials form complex networks of force chains arising from frictional interactions between particles. Under applied shear, this network of contacts can undergo complex topological and geometrical rearrangements. The connection between these grain-scale patterns and the macroscopic behavior of the material is still a field of active research. In this project we will employ Graph Neural Networks (GNNs) to shed light on these processes, focusing on the regime where granular materials approach unjamming and failure. The models will be trained on data from high fidelity discrete
element simulations as well as experimental measurements with grain-scale resolution.
More details can be found here.