Blar i NTNU Open på forfatter "Grimstad, Bjarne André"

Viser treff 1-7 av 7

    • Global optimization of multiphase flow networks using spline surrogate models 

      Grimstad, Bjarne André; Foss, Bjarne Anton; Heddle, Richard; Woodman, Malcolm (Journal article; Peer reviewed, 2016)
      A general modelling framework for optimization of multiphase flow networks with discrete decision variables is presented. The framework is expressed with the graph and special attention is given to the convexity properties ...

    • Multi-task neural networks by learned contextual inputs 

      Sandnes, Anders Thoresen; Grimstad, Bjarne André; Kolbjørnsen, Odd (Journal article; Peer reviewed, 2024)
      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 ...

    • Petroleum production optimization - A static or dynamic problem? 

      Foss, Bjarne Anton; Knudsen, Brage Rugstad; Grimstad, Bjarne André (Journal article; Peer reviewed, 2018)
      This paper considers the upstream oil and gas domain, or more precisely the daily production optimization problem in which production engineers aim to utilize the production systems as efficiently as possible by for instance ...

    • Semi-supervised learning for well flow modelling 

      Løvland, Kristian (Master thesis, 2021)

    • Spline Fluid Models for Optimization 

      Jahanshahi, Esmaeil; Grimstad, Bjarne André; Foss, Bjarne Anton (Journal article; Peer reviewed, 2016)

    • Studies in Output Feedback Control 

      Grimstad, Bjarne André (Master thesis, 2009)
      This master s thesis studies the recently proposed initialization scheme for the H2-optimal static output feedback problem [1]. The initialization scheme consists of solving a convex quadratic programming (QP) problem to ...

    • When is gray-box modeling advantageous for virtual flow metering? 

      Hotvedt, Mathilde; Grimstad, Bjarne André; Ljungquist, Dag; Imsland, Lars Struen (Peer reviewed; Journal article, 2022)
      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 predictions. However, the superiority of gray-box ...