Home/Research/Digital Twins

Digital Twins

Virtual replicas enabling real-time monitoring, simulation, and optimization

Digital Twins for Buildings

Overview

Digital Twin technology creates virtual replicas of physical systems that enable real-time monitoring, simulation, and optimization. Our research focuses on developing comprehensive digital twin frameworks for various applications including wind farms, autonomous vessels, and urban infrastructure.

As coordinator of the NTNU Digital Twin team, I lead efforts to establish standardized frameworks and methodologies for digital twin development across multiple domains.

Digital twin classification

Standalone DT

Design and simulation Requires no physical asset or live sensor/operational data, is built from CAD and high-fidelity simulations, and can be informed by learnings from historical or previous-generation assets.

Descriptive DT

Real-time description Ingests sparse real-time sensor data, uses models to improve data quality and enhance spatio-temporal resolution, computes quantities of interest, and delivers on-demand views of the asset’s past and current state.

Diagnostic DT

Deviation from norm Continuously monitors current asset health, fuses sensor data with computationally efficient models to detect known faults and emerging anomalies, isolate likely root causes, and provides actionable insights.

Predictive DT

Forecasting the future Uses past and present observations of the asset and computationally efficient knowledge-based and/or data-driven models to forecast the asset’s future state in real time with quantified uncertainty.

Prescriptive DT

CUses scenario analyses, risk assessments, and uncertainty quantification to produce ranked recommendations for the asset, offering actionable guidance without autonomously executing decisions.

Autonomous DT

Self-learning control and execution Closes the control loop by continuously monitoring the asset, deciding and executing actions in real time with minimal human intervention, and optimizing performance as conditions evolve.

Key Projects

Wind Energy

NorthWind

Digital twin framework for wind farms (320M NOK, 2021-2028)

Autonomous Systems

SFI Autoship

Situational awareness for autonomous ships (240M NOK, 2020-2023)

Laboratory Twin

PoroTwin

Digital twin of porous-media experiments (FluidFlower rig) for validating multi-physics models.

Agritech

Smart Greenhouse

Smart greenhouse digital twins for climate control, automation, and yield optimization.

Key Publications

Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., Kvamsdal, T.
IEEE Access, 8, 21980-22012, 2020
IEEE
Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twins
Rasheed, A., Raavik, Oscar, San, O.
arXiv
arXiv
Digital Twin Ready Encapsulated Thermal Lab: A Modular System for Remote Thermal Imaging and Condition Monitoring
Jordheim, H., Stadtmann, F., Westad, F., Rasheed, A.
Submitted to Hardware X
SSRN
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging
Menges, D., Stadtman, Jordheim, H. and Rasheed, A.
arXiv
arXiv
Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation
Menges, D. and Rasheed, A.
Submitted / arXiv
arXiv
Communication Efficient Digital Twin-Based Federated Anomaly Detection for Industrial IoT
Belay, M.A., Rasheed, A., Rossi, P.S.
Submitted
A Data-Driven Building Thermal Zoning Algorithm for Digital Twin-Enabled Advanced Control
Morkunaite, L., Rasheed, A., Pupeikis, D., Angelakis, V., Davidsson, T.
Energy and Buildings, 336, 115633, 2025
DOI
Digital Twin Syncing for Autonomous Surface Vessels Using Reinforcement Learning and Nonlinear Model Predictive Control
Berg, H.S., Menges, D., Tengesdal, T., Rasheed, A.
Scientific Reports, 15, 9344, 2025
DOI
Physics-guided federated learning as an enabler for digital twin
Stadtmann, F., Furevik, E., Rasheed, A., Kvamsdal, T.
Expert Systems with Applications, 125169, 2024
DOI
Digital twins in intensive aquaculture - Challenges, opportunities and future prospects
Føre, M., Alver, M.O., Alfredsen, J.A., Rasheed, A., et al.
Computers and Electronics in Agriculture, 218, 108676, 2024
DOI
PoroTwin: A Digital Twin for a FluidFlower Rig
Keilegavlen, E., Fonn, E., Johannessen, K., Eikehaug, K., Both, J.W., Fernø, M., Kvamsdal, T., Rasheed, A., Nordbotten, J.M.
Transport in Porous Media, 151, 1241–1260, 2024
DOI
Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality
Elfarri, E.M., Rasheed, A., San, O.
IEEE Access, 11, 35035-35058, 2023
DOI
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution
San, O., Rasheed, A., Kvamsdal, T.
GAMM Mitteilungen, 44, e202100007, 2021
DOI
Geometric change detection in digital twins
Sundby, T., Graham, J. M., Rasheed, A., Tabib, M., San, O.
Digital, 1 (2), 111-129, 2021
DOI
Digital Twin Knowledge Distillation for Federated Semi-Supervised Industrial IoT DDoS Detection
Belay, M.A., Rasheed, A., Rossi, P.S.
2025 IEEE Symposium on Computational Intelligence in Security, Defence and Biometrics Companion (CISDB Companion), Trondheim, Norway, 2025
Link
Digital Twin-Based Federated Transfer Learning for Anomaly Detection in Industrial IoT
Belay, M.A., Rasheed, A., Rossi, P.S.
2025 IEEE Symposium on Computational Intelligence on Engineering/Cyber Physical Systems (CIES), Trondheim, Norway, 2025
Link
Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled Through Predictive Modeling and Reinforcement Learning
Menges, D., Von Brandis, A., Rasheed, A.
Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. Singapore, June 9–14, 2024.
DOI
Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy
Stadtmann, F., Rasheed, A.
Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. Singapore, June 9–14, 2024.
DOI
Digital Twin for Wind Energy: Latest Updates From the NorthWind Project
Rasheed, A., Stadtmann, F., Fonn, E., Tabib, M., Tsiolakis, V., Panjwani, B., et al.
Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. Singapore, June 9–14, 2024.
DOI
A Digital Twin for Reservoir Simulation
Keilegavlen, E., Fonn, E., Johannessen, K., Tegnander, T., Eikehaug, K., Both, J.W., Fernø, M.A., Kvamsdal, T., Rasheed, A., Eigestad, G.T., Nordbotten, J.M.
SPE Norway Subsurface Conference, Bergen, Norway, April 2024
DOI
Data Integration Framework for Virtual Reality Enabled Digital Twins
Stadtmann, S., Mahalingam, H.P., Rasheed, A.
2023 IEEE WF-IoT, Aveiro, Portugal
Link
Data-Driven Spatio-Temporal Modelling and Optimal Sensor Placement for a Digital Twin Set-Up
Tabib, M., Skare, K., Bruaset, E., Rasheed, A.
Eng. Proc. 2023, 39, 98
Link
Standalone, Descriptive, and Predictive Digital Twin of an Onshore Wind Farm in Complex Terrain
Stadtman, F., Rasheed, A., Rasmussen, T.
Journal of Physics Conference Series, 2626, 012030
Link
Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness
Menges, D., Sætre, S.M., Rasheed, A.
ASME OMAE 2023, Melbourne, Australia
DOI
Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbine
Stadtmann, F., Wassertheurer, H.A.G., Rasheed, A.
ASME OMAE 2023, Melbourne, Australia
DOI
Hybrid Analysis and Modeling for Next Generation of Digital Twins
Pawar, S., Ahmed, S.E., San, O., Rasheed, A.
Journal of Physics: Conference Series, 2018(1):012031 (published 2021)
Link