5 citations · 8 across the 8 of their papers we have counts for
4 papers · 1 filter
Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC
Niels Krausch, Martin Doff-Sotta, Mark Canon +2
Bioprocesses are often characterized by nonlinear and uncertain dynamics. This poses particular challenges in the context of model predictive control (MPC). Several approaches have…
Latent State Space Extension for interpretable hybrid mechanistic models
Judit Aizpuru, Maxim Borisyak, Peter Neubauer +1
Mechanistic growth models play a major role in bioprocess engineering, design, and control. Their reasonable predictive power and their high level of interpretability make them an…
Deep Learning for Fast Inference of Mechanistic Models' Parameters
Maxim Borisyak, Stefan Born, Peter Neubauer +1
Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engi…
Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries
Maxim Borisyak, Stefan Born, Peter Neubauer +1
Cultivation experiments often produce sparse and irregular time series. Classical approaches based on mechanistic models, like Maximum Likelihood fitting or Monte-Carlo Markov chai…