About us
Adil Rasheed
I am the Professor of Bigdata Cybernetics at the Department of Engineering Cybernetics, Norwegian University of Science and Technology. In the role I am developing new methods for combining the physics based modeling with the data driven modeling. I also hold a part time position as a Senior Scientist at the Mathematics and Cybernetics Department of SINTEF Digital.
Email: adil.rasheed[@]ntnu.no
Mobile: +47-90291771
Ph.D.
It will be impossible for me to pursue my very wide array of interest without the help of some of my brilliant group members 🙂
Oluwaleke Umar Yusuf
2023-2026 (Sup)
Future mobility solution
Mehmet Cagri Altindal
2023-2026 (Sup)
Hybrid and Machine Learning method development for hole cleaning monitoring
Karl Johan Haarberg
2022-2025 (Sup)
Digitalization of Bussiness Processses
Daniel Menges
2022-2025 (Sup)
Digital Twin for Autonomous Ships
Alberto Mino
2021-2024 (Sup)
Explainable Artificial Intelligence
Florian Stadman
2021-2024 (Sup)
Enabling Technologies for Digital Twins
Thomas Nakken Larsen
2020-2024 (Sup)
Learning in a Digital Twin Environment
Mohammed Ayalew Belay
2021-2024 (Co-Sup)
Anomaly detection in timeseries data
Even Klemsdal
2021-2024 (Co-Sup)
Multiagent reinforcement learning
Håvard Bjørgan Bjørkøy
2020-2024 (Co-sup)
Big data cybernetics for the process industry
Roya Doshmanziari
2020-2023 (Co-sup)
Biofeedback
Hans A. Engmark
2021-2023 (Co-sup)
Big data cybernetics for the process industry
Erlend Lundby
2019-2023 (Co-sup)
Hybrid Modeling in Process Industry
Haakon Robinson
2019-2023 (Sup)
Hybrid Analysis and Modeling
Wanwan Zhang
2021-2024 (Co-sup)
Predictive Maintenence
Abdallah Alshantti
2019-2022 (Co-sup)
AI in financeMoney
Prateek Gupta
2018-2022 (Co-sup)
Ship performance monitoring using in-service measurement data
Zakari Midjiyawa
2018-2022 (Co-sup)
Turbulence characterization in complex fjord using measurement and numerical modeling for bridge design.
Muhammad Salman Siddiqui
2015-2019 (Co-sup)
High fidelity simulation and reduced order modeling of wind turbines
MSc
I have had the privilendge to work with some of the best masters students. Here are topics of their thesis
Anne Willkommen Eiken
(2022)
Job Title
Position Alignment and Geographical Location Determination of Railway Track Condition Monitoring Data
Annfrid Hopland Myklebust
(2022)
Job Title
Building a digital twin of the thermodynamic behaviour of a building using hybrid modeling
Elias Mohammed Elfarri
(2022)
Job Title
Digital Twin of a Building Powered by Artificial Intelligence and Demonstrated in Virtual Reality
Kristian Brudeli
(2022)
Job Title
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Lars Gjardar Musæus
(2022)
Job Title
Fractal Analysis and Its Application on Time-Series Data – An Innovative Method for Condition Monitoring of Hole Cleaning Operations
Ludvig Løken Sundøen
(2022)
Job Title
Path Following for Quadcopters using Deep Reinforcement Learning
Marcus Skagemo
(2022)
Job Title
Improved market entry of long-term time horizon trading signals using short-term residual reversal
Simon Mork Sætre
(2022)
Job Title
Laying The Foundation For an Artificial Intelligence-Powered Extendable Digital Twin Framework For Autonomous Sea Vessels
Sondre Sorbø
(2022)
Job Title
Corrective Source Term Approach for improving Erroneous Physics-Based Models
Katarina Charlotte Guderud
(2021)
Raa Labs
Predicting feeding patterns in aquaculture
Tiril Sundby
(2021)
Bouvet ASA
Towards Geometric Change Detection in Digital Twins using Dynamic Mode Decomposition, Object Detection and 3D Machine Learning
Julia Graham
(2021)
Cognite
Geometric change detection in digital twins
Olav Pedersen
(2021)
Prediktor
A proof-of-concept Digital Twin implementation for monitoring patients through the Clinical Pathway for Prostate Cancer in the Norwegian Health and Care Service
Bendik Austnes
(2021)
NoA Ignite
Increasing Validity and Uncovering Utility in Machine Learning Studies: An Illustrative Approach to Essential Concepts and Procedures in Model Development and Assessment
Sindre Stenene Blakseth
(2021)
SINTEF Energy
Introducing CoSTA: A Deep Neural Network Enabled Approach to Improving Physics-Based Numerical Simulations
Viljar Ness
(2021)
Sopra Steira
Simulating Differential Equations using Physics Guided Machine Learning
Ole Jørgen Hennestad
(2021)
Firda
Securing trust in AI systems through increased explainability: linking Norwegian organizations’ challenges in regards to eXplainable Artificial Intelligence (XAI) with a 2021 view on relevant techniques and methods
Andrine Elsetrønning
(2021)
Sopra Steira
Generalized workflow with uncertainty quantification for detecting abnormalities in lung sounds
Marie Skatvedt
(2021)
Norkart AS
Machine Learning for bottom-detection in Doppler Velocity Logs
Fredrik Pettersen Sandvik
(2021)
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Applications of Data-Driven Equation Discovery to Synthetic and Experimental Data
Torkel Laache
(2021)
Job Title
Physics Guided Machine Learning: Injecting neural networks with simplified theories
Vebjørn Malmin
(2021)
Bredvid AS
Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control
Halvor Ødegård Teigen
(2021)
Sopra Steria
Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control
Simen Havenstrom
(2020)
Equinor
From Beginner to Expert: Deep Reinforcement Learning Controller for 3D Path-Following and Collision Avoidance by Autonomous Underwater Vehicles
Thomas Nakken Larsen
(2020)
PhD student at NTNU
The applicability of perceptually driven generative-adversarial networks for super-resolution of wind fields in complex terrain
Peter Herman Stavelin
(2020)
Penetrace
Object Detection Applied to Marine Data for Utilizing Optic-Acoustic Optimization
Eirik Ekjord Vesterkjær
(2020)
Kongsberg Digital
Combining Grid-based Uncertainty Propagation and Neural Networks with Uncertainty Estimation
Amalie Heiberg
(2020)
Equinor
COLREG-Compliance for Autonomous Surface Vehicles using Deep Reinforcement Learning
Duy Tan Tran
(2020)
Netlight
Convolutional Neural Network and Generative Adversarial Networks Enabled Resolution Enhancement of Numerical Simulations
Eivind Meyer
(2020)
PhD Student at TUM
On Course Towards Model-Free Guidance: A Self-Learning Approach To Dynamic Collision Avoidance for Autonomous Surface Vehicles
Daniel Nakken
Job Title
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Haakon Robinson
Job Title
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News
Highlights from my research group
Research
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Digital Twins
A digital twin is defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making.
Hybrid Analysis and Modelling
HAM / HM / HA is a new paradigm in modeling that combines the interpretability, robust foundation and understanding of a physics-based approach with the accuracy, efficiency, and automatic pattern-identification capabilities of advanced data-driven machine learning and artificial intelligence algorithms to produce less uncertain results.
Physics-based modeling
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Machine Learning and Artificial Intelligence
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Design of Experiment
Desgning experimental set-up to test methods, design of experiments to collect data with minimum effort
Application
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Wind Energy
Projects: NorthWind, OPWIND, FSI-WT
Theme: Computational fluid dynamics, Solid mechanics, fluid-structure interaction, high performance computing, reduced order modeling, hybrid analysis and modeling, Digital Twin, Asset Management
Built Environment
Project: Multiscale modeling of urnal climate
Theme: Atmospheric modeling, Building physics, Digital twin, Sensor data analysis
Autonomous ships
Projects: SFI Autoship, Aitosit
Theme: Situational awareness, digital twin
Methods: Reinforcement learning, Computer vision, DIgital twin
Oil and Gas
Project: Hole Cleaning Monitoring in drilling with distributed sensors and hybrid methods
Theme: Drilling, Hybrid modeling
Process Industry
Project: Towards autonomy in process industries
Theme: Hybrid Modeling, Sparse data modeling, Machine Learning and Artificial Intelligence, Aluminium extraction
Paintings
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Acrylic painting
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Pastel Painting
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Oil painting
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Watercolor painting
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