Explores data-driven modeling of haemodynamics in vascular flows, focusing on computational challenges, reduced order modeling, FSI problems, and neural network applications.
Explores atomic descriptors, emphasizing symmetry, locality, and the challenges of incorporating electrostatics in machine learning models for chemistry.