Surrogate Modeling of CFD Simulation of Bio-processes Using Machine Learning

Mathematical modeling of pharmaceutical processes helps scientist to design efficient and high quality production processes. Physics-based models are typically time-consuming to perform, therefore hybrid approaches with the data-driven models holds a great potential to increase the use of such models. In our research, we focus on developing surrogate models for the CFD simulations of bio-processes using machine learning.