Publikasjoner
- Physics-guided federated learning as an enabler for digital twins
- Modular control architecture for safe marine navigation: Reinforcement learning with predictive safety filters
- Federated Learning and Unlearning as Enablers of Wind Turbine Digital Twins
- Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions Les publikasjonen
- Digital Twin for Wind Energy: Latest Updates From the NorthWind Project
- Multivariate Time-Series Methods with Uncertainty Estimation for Correcting Physics-Based Model: Comparisons and Generalization for Industrial Drilling Process
- Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled Through Predictive Modeling and Reinforcement Learning
- Anomaly detection in multivariate time series of drilling data
- Privacy Re-Identification Attacks on Tabular GANs
- Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data
Annen formidling
- Predictive Digital Twin for Condition Monitoring Using Thermal Imaging
- Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels
- Digital Twins in aquaculture: Taking Precision Farming to the next level
- Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data
- Digital Twins in Wind Energy
- Data Integration Framework for Virtual Reality Enabled Digital Twins
- Reduced Order Modelling (ROM) and Hybrid Analysis and Modelling (HAM) as Enablers for Predictive Digital Twins (DT)
- PoroTwin: A Digital Twin for a Meter-Scale Porous Medium
- Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness
- PoroTwin: A Digital Twin for a Meter-Scale Porous Media
