5 papers · 1 filter
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…
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…
Passive learning to address nonstationarity in virtual flow metering applications
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Steady-state process models are common in virtual flow meter applications due to low computational complexity, and low model development and maintenance cost. Nevertheless, the pre…
When is gray-box modeling advantageous for virtual flow metering?
M. Hotvedt, B. Grimstad, D. Ljungquist +1
Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predict…
Identifiability and physical interpretability of hybrid, gray-box models -- a case study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Model identifiability concerns the uniqueness of uncertain model parameters to be estimated from available process data and is often thought of as a prerequisite for the physical i…