L.F. (Leon) Riccius, MSc.
I studied Mechanical Engineering at the Technical University of Munich and graduated in 2021. I specialized on numerical approaches to partial differential equations such as the Finite Element Method and Discontinuous Galerkin Schemes. Through my Master’s thesis, where I investigated the potential of Neural Networks for Reynolds-Averaged Navier-Stokes closure modelling, I expanded into the field of physics-informed machine learning.
As part of the newly formed SLIMM Lab, I am applying the latest breakthroughs in Bayesian inverse modelling and sampling techniques to the simulation of composite materials. My Ph.D. project on Bayesian Machine Learning for Multiscale Modeling of 3D-printed Materials addresses the Bayesian calibration of the developed material models with observations coming from both experiments and multiscale simulations.