Dr. Gottfried Hastermann

About the person

As an applied mathematician in the Mathematics of Data Science group, my primary research interest lies in numerical analysis at the intersection of observed data and (partial) differential equations describing first principles. Specifically, I focus on data assimilation of transport-dominated physical phenomena such as fluid flows in atmospheric systems.
As part of this work, I develop and implement algorithms and also provide rigorous bounds and error estimates. In line with the challenges presented by the intrinsic high dimensionality and sparse data in the atmospheric setting, I am additionally working on reinforcement learning for data augmented, partially observed Markov decision processes where data and models originate from clinical research and human biological systems respectively.