Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Grimstad, Bjarne"'
Deep sequence models are receiving significant interest in current machine learning research. By representing probability distributions that are fit to data using maximum likelihood estimation, such models can model data on general observation spaces
Externí odkaz:
http://arxiv.org/abs/2409.04120
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 more data-efficient. To achieve this, one can leverage knowledge abou
Externí odkaz:
http://arxiv.org/abs/2407.13310
In petroleum production systems, continuous multiphase flow rates are essential for efficient operation. They provide situational awareness, enable production optimization, improve reservoir management and planning, and form the basis for allocation.
Externí odkaz:
http://arxiv.org/abs/2404.06328
Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. A par
Externí odkaz:
http://arxiv.org/abs/2309.15828
Soft-sensors are gaining popularity due to their ability to provide estimates of key process variables with little intervention required on the asset and at a low cost. In oil and gas production, virtual flow metering (VFM) is a popular soft-sensor t
Externí odkaz:
http://arxiv.org/abs/2304.06310
This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters. The architecture is interesting due to its powerf
Externí odkaz:
http://arxiv.org/abs/2303.00788
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 by control are accounted for. Using recent results from work on struc
Externí odkaz:
http://arxiv.org/abs/2211.05613
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 prediction performance of steady-state models typically degrades with tim
Externí odkaz:
http://arxiv.org/abs/2202.03236
Publikováno v:
In Neural Networks November 2024 179
Publikováno v:
Control Engineering Practice, volume 118, 2022
A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. The predicted flow rates may aid the daily control and optimization of a petroleum asset. Gray-box modeling is an approach that combines mechanist
Externí odkaz:
http://arxiv.org/abs/2103.12513