Publikasjoner
- Machine learning (ML) based reduced order modelling (ROM) for linear and non-linear solid and structural mechanics Les publikasjonen
- Least‐Squares Projected Models for Non‐Intrusive Affinization of Reduced Basis Methods Les publikasjonen
- Har vi råd til havvind i Norge? Les publikasjonen
- Enhanced Sensor Placement Optimization and Defect Detection in Structural Health Monitoring Using Hybrid PI-DEIM Approach Les publikasjonen
- An efficient and easy-to-implement recovery-based a posteriori error estimator for isogeometric analysis of the Stokes equation Les publikasjonen
- Digital Twin for Wind Energy: Latest Updates From the NorthWind Project
- Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions Les publikasjonen
- Physics-guided federated learning as an enabler for digital twins
- Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions Les publikasjonen
- PoroTwin: A Digital Twin for a FluidFlower Rig Les publikasjonen
Annen formidling
- Computational Modelling of Heat Transfer in Liquid Hydrogen Containment Systems Les publikasjonen
- COP30: Offshore Wind – SINTEF Blog
- COP30: Havvind – SINTEF-blogg
- Workshop on innovations for PhD students focuses on knowledge sharing and collaboration
- Reduced Order Modelling (ROM) and Hybrid Analysis and Modelling (HAM) as Enablers for Predictive Digital Twins (DT)
- CONWIND – research on smart operation control technologies for offshore wind farms
- PoroTwin: A Digital Twin for a Meter-Scale Porous Media
- PoroTwin: A Digital Twin for a Meter-Scale Porous Medium
- Adaptive Isogeometric Analysis of Thin Shell Problems with Kirchhoff–Love Elements
- Deep Neural Network Enabled Corrective Source Term Approach for Predictive Digital Twins