Publikasjoner
- Digital Twin for Wind Energy: Latest Updates From the NorthWind Project
- Deep learning based hybrid POD-LSTM framework for laminar natural convection flow in a rectangular enclosure
- Multivariate Time-Series Methods with Uncertainty Estimation for Correcting Physics-Based Model: Comparisons and Generalization for Industrial Drilling Process
- Anomaly detection in multivariate time series of drilling data
- Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach
- 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
Annen formidling
- 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
- Screening thermoelectric materials with ab initio atomistic modelling and machine learning techniques