5 papers
On a closed-loop identification challenge in feedback optimization
Kristian Lindbäck Løvland, Lars Struen Imsland, Bjarne Grimstad
Feedback optimization has emerged as an effective strategy for steady-state optimization of dynamical systems. By exploiting models of the steady-state input-output sensitivity, me…
An updated look on the convergence and consistency of data-driven dynamical models
Kristian Løvland, Bjarne Grimstad, Lars Struen Imsland
Deep sequence models are receiving significant interest in current machine learning research. By representing probability distributions that are fit to data using maximum likelihoo…
A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes
Bjarne Grimstad, Kristian Løvland, Lars S. Imsland +1
In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being…
Multi-task and few-shot learning in virtual flow metering
Kristian Løvland, Bjarne Grimstad, Lars S. Imsland
Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge i…
Adjustment formulas for learning causal steady-state models from closed-loop operational data
Kristian Løvland, Bjarne Grimstad, Lars Struen Imsland
Steady-state models which have been learned from historical operational data may be unfit for model-based optimization unless correlations in the training data which are introduced…