Adil Rasheed

Adil Rasheed is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. His research focuses on digital twin technology, artificial intelligence (AI), machine learning (ML), physics-based modeling, and hybrid analysis methodologies. He leads projects such as the work package on Digital Twin and Asset Management, collaborating with major industry partners to advance digital twin capabilities. His interdisciplinary work bridges AI-driven approaches with traditional physics-based models for applications in wind energy, autonomous vessels, smart greenhouses, and aquaculture. Notable contributions include developing a VR-enabled smart greenhouse digital twin and the PoroTwin framework for porous media flow analysis. His recent publications span topics like safe marine navigation using reinforcement learning, federated learning for industrial IoT anomaly detection, and predictive maintenance in offshore wind turbines. Rasheed actively engages in academic outreach and serves as an advisor to research initiatives in hybrid modeling and autonomous systems.

Research Interests

• Creation and application of digital twins for physical systems optimization
• Integration of AI/ML with physics-based models for hybrid systems
• Autonomous systems navigation and safety (vessels, drones)
• Wind energy systems and offshore renewable energy
• Condition monitoring and predictive maintenance
• Data-driven solutions for aquaculture and urban mobility challenges

Key Projects

• Smart Greenhouse: AI-driven digital twin for autonomous plant growth monitoring via VR
• PoroTwin: Digital twin for porous media flow analysis in oil and gas
• Hybrid Analysis and Modeling (HAM) framework combining knowledge-based and data-driven methods
• NorthWind Project: Digital twin advancements for wind energy systems

Publications Trends

Rasheed's recent work emphasizes digital twin applications in energy systems (wind turbines), autonomous maritime navigation, and industrial IoT security. He explores federated learning techniques for decentralized anomaly detection and integrates reinforcement learning for safety-critical control systems. Collaborations with industry partners highlight practical implementations in offshore infrastructure, aquaculture, and urban mobility.

Grants & Partnerships

Active collaborations with industry leaders in renewable energy, maritime robotics, and smart agriculture ensure his research addresses real-world challenges. His work is supported by interdisciplinary projects combining academia-industry expertise.

Labs & Teams

Leads the Digital Twin and Asset Management team at NTNU, coordinating with research groups in computational engineering, autonomous systems, and renewable energy technology.