摘要：AbstractPhenotypic variability in cell populations can be described through population balance models (PBM). In this paper, the simulation of cell PBMs via two different methods is compared. We compare the cell average technique (CAT) with the recently introduced numerical Gaussian processes (GPs). While the CAT discretises the PBM in the individual variables, i.e. in the cellular properties, numerical GPs circumvent this discretisation. Numerical GPs are a probabilistic machine-learning method that additionally provides uncertainty quantification. Using two simulation case studies, we compare both methods with regards to computational load, accuracy, and implementation effort. In one of the two considered case studies, numerical GPs are inferior to the CAT. Nonetheless, they are an attractive option when uncertainty quantification is very much required, such as in a subsequent state estimation for PBMs.
