Publikasjoner
- Digital Twin for Wind Energy: Latest updates from the NorthWind project
- Anomaly detection in multivariate time series of drilling data
- A Reinforcement Learning framework for Wake Steering of Wind Turbines
- Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models
- An Intrusive Hybrid-Analytics and Modelling with Deep-Learning for Efficient and Accurate Predictions of Hole-Cleaning Process during Wellbore Drilling Simulations
- Data-Driven Spatio-Temporal Modelling and Optimal Sensor Placement for a Digital Twin Set-Up
- A Hybrid Approach to Detect Bad Hole Cleaning
- Machine Learning for Capacity Utilization Along the Routes of an Urban Freight Service
- Hybrid deep-learning POD-based parametric reduced order model for flow around wind-turbine blade Les publikasjonen
- Geometric Change Detection in Digital Twins Les publikasjonen
Annen formidling
- Machine Learning for enhancing Wind Field Resolution in Complex Terrain
- Role of Mathematical Modeling in Advanced Power Generation Systems
- Hybrid Analytics and Modellling Applications in Fluid Flow: Case Study - Wind Energy, Drilling, Greenhouse and Urban City.
- An Intrusive Hybrid-Analytics and Modelling with Deep-Learning for Efficient and Accurate Predictions of Hole-Cleaning Process during Wellbore Drilling Simulations
- Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models
- A Hybrid Approach to Detect Bad Hole Cleaning
- Droner og AI - forskning og innovasjon med SINTEF
- Droner og AI - utvikling og innovasjon med SINTEF
- Droner og AI - utvikling og innovasjon med SINTEF
- Robotics for inspection and maintenance