Data Science Fellow
Michigan Institute for Data Science
The field of computational fluid dynamics (and more broadly, computer simulations) give us tools to help understand and study scientific/engineering problems without the need to run many costly, real life experiments. However, because the mathematical models underlying these simulations are (often) simplifications of the real processes of interest, their modelling/predictive power depends on the simulation’s resolution, and time or spatial scales of the problem of interest. During my Postdoc at the MIDAS, I will work towards developing techniques for multi-scale modelling through assimilation of experimental data (to account for the simplifications of the models), and towards developing machine learning techniques compliant with a set of constraints (for example, physics laws).