Adil Rasheed

Professor

Norwegian University of Science and Technology

Norway

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Dr. Adil Rasheed is a Professor at the Norwegian University of Science and Technology. His research primarily focuses on dynamic planning, AI applications for urban mobility, and advanced control systems, as reflected in his recent publications. In particular, he explores innovative approaches to sustainable travel, anomaly detection in time series data, and enhancing predictive models using machine learning techniques. His work contributes significantly to the fields of urban environmental management and autonomous navigation.

Adil Rasheed

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Articles (20)

Leveraging Big Data and AI for Sustainable Urban Mobility Solutions

Resource-constrained dynamic planning and model-free control in turbulent urban environments

A data-driven building thermal zoning algorithm for digital twin-enabled advanced control

Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control

Sustainable Active Travel in Environmentally Challenging Cities: A Systematic Review of Barriers and Strategies

Sparse Non-Linear Vector Autoregressive Networks for Multivariate Time Series Anomaly Detection

Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach

Modular control architecture for safe marine navigation: Reinforcement learning with predictive safety filters

MTAD: Multiobjective Transformer Network for Unsupervised Multisensor Anomaly Detection

Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions

Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality

Deep learning assisted physics-based modeling of aluminum extraction process

Data-Driven Spatio-Temporal Modelling and Optimal Sensor Placement for a Digital Twin Set-Up

Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions

Ship performance monitoring using machine-learning

Physics guided neural networks for modelling of non-linear dynamics

Prospects of federated machine learning in fluid dynamics

Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach

Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning

Deep neural network enabled corrective source term approach to hybrid analysis and modeling

Collaborators (5)

Omer San

Frank Lindseth

Sverre Steen

Jan Tommy Gravdahl

Damiano Varagnolo

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