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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.
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.
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