Publikasjoner
- Digital Twins in intensive aquaculture — Challenges, opportunities and future prospects Les publikasjonen
- Anomaly detection in multivariate time series of drilling data
- Privacy Re-identification Attacks on Tabular GANs
- Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
- Unveiling Urban Mobility Patterns: A Data-Driven Analysis of Public Transit
- Exploring Urban Mobility Trends using Cellular Network Data
- Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data
- Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters
- 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
Annen formidling
- Machine Learning for enhancing Wind Field Resolution in Complex Terrain
- Digital Twins in Wind Energy
- 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
- Modular Collision Avoidance Using Predictive Safety Filters
- Data Integration Framework for Virtual Reality Enabled Digital Twins
- Hybrid Analytics and Modellling Applications in Fluid Flow: Case Study - Wind Energy, Drilling, Greenhouse and Urban City.
- Digital Twinning of Autonomous Systems