Blar i SINTEF Open på forfatter "Eidnes, Sølve"

Viser treff 1-5 av 5

    • Analysis of full-scale riser responses in field conditions based on Gaussian mixture model 

      Wu, Jie; Eidnes, Sølve; Jin, Jingzhe; Lie, Halvor; Yin, Decao; Passano, Elizabeth Anne; Sævik, Svein; Riemer-Sørensen, Signe (Peer reviewed; Journal article, 2022)
      Offshore slender marine structures experience complex and combined load conditions from waves, current and vessel motions that may result in both wave frequency and vortex shedding response patterns. Field measurements ...

    • Data quality issues for vibration sensors: a case study in ferrosilicon production 

      Waszak, Maryna; Moen, Terje; Eidnes, Sølve; Stasik, Alexander Johannes; Hansen, Anders; Bouquet, Gregory; Pultier, Antoine; Ma, Xiang; Tørlen, Idar; Henriksen, Bjørn; Aamodt, Arianeh; Roman, Dumitru (Chapter, 2022)
      Digitisation in the mining and metal processing industries plays a key role in their modernisation. Production processes are more and more supported by a variety of sensors that produce large amounts of data that meant to ...

    • Order theory for discrete gradient methods 

      Eidnes, Sølve (Peer reviewed; Journal article, 2022)
      The discrete gradient methods are integrators designed to preserve invariants of ordinary differential equations. From a formal series expansion of a subclass of these methods, we derive conditions for arbitrarily high ...

    • Pseudo-Hamiltonian neural networks for learning partial differential equations 

      Eidnes, Sølve; Lye, Kjetil Olsen (Peer reviewed; Journal article, 2024)
      Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend the method to partial differential equations. ...

    • Pseudo-Hamiltonian system identification 

      Holmsen, Sigurd; Eidnes, Sølve; Riemer-Sørensen, Signe (Peer reviewed; Journal article, 2024)
      Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as first-order ordinary differential equations. ...