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Found 9 papers, 0 papers with code
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
This is of interest in scenarios where large amounts of historical data are available but are currently not used for model training.
Latent State Space Extension for interpretable hybrid mechanistic models
We demonstrate the framework's capabilities by fitting a hybrid model based on a simple mechanistic growth model for E. coli with data generated in-silico by a much more complex one and identifying missing kinetics.
Deep Learning for Fast Inference of Mechanistic Models' Parameters
We consider a training procedure that combines Neural Networks and mechanistic models.
Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries
The method is agnostic to the particular nature of the time series and can be adapted for any task, for example, online monitoring, predictive control, design of experiments, etc.
Handling nonlinearities and uncertainties of fed-batch cultivations with difference of convex functions tube MPC
Bioprocesses are often characterized by nonlinear and uncertain dynamics.
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks.
Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of \textit{E. coli} fed-batch cultivations with intermittent bolus feeding.
Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on DynamicTime Warping
TheDissolved Oxygen Tension is often the only measurement which is available online, and therefore, a good understanding of the errors in this signal is important for performing a robust estimation. Some of the expected errors will provoke uncertainties in the time-domain of the measurement, and in those cases, the common Weighted Least Squares estimation procedure can fail providing good results.
Model predictive control guided with optimal experimental design for pulse-based parallel cultivation
Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model.
