Computational Fluid Mechanics is a field in engineering, in which the computer is used to solve mathematical equations that describe the behavior of fluids like air or water. The just mentioned solution process is typically called “simulation”. The objective of this research topic is to investigate the application of Machine Learning models in fluid simulations. That is, the typically expensive simulation process is replaced by a Machine Learning problem.
This research project touches the following topics:
- Modeling of fluids by the Navier-Stokes equations
- use of an existing Navier Stokes fluid solver to generate training snapshots
- further development of machine learning techniques for prediction of outcomes of fluid simulations
- time-series prediction / quantity of interest prediction / spatial prediction
This is another hot topic, at least in the “simulation business”. Research-relevant questions are:
- Can we find ML models that nicely predict bifurcation-like behavior?
- Can we use ML models as sub-models (homogenization-like) in bigger models?
Here some links: