Blar i Institutt for teknisk kybernetikk på tidsskrift "Applied Soft Computing"
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Bayesian neural networks for virtual flow metering: An empirical study
(Peer reviewed; Journal article, 2021)Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as ... -
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
(Peer reviewed; Journal article, 2022)Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety–critical applications require accurate, interpretable, computationally efficient, and generalizable models. ... -
Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions
(Peer reviewed; Journal article, 2024)With the recent wave of digitalization, specifically in the context of safety–critical applications, there has been a growing need for computationally efficient, accurate, generalizable, and trustworthy models. Physics-based ... -
Sparse deep neural networks for modeling aluminum electrolysis dynamics
(Peer reviewed; Journal article, 2023)Deep neural networks have become very popular in modeling complex nonlinear processes due to their extraordinary ability to fit arbitrary nonlinear functions from data with minimal expert intervention. However, they are ...