About Me

I am the Professor of Bigdaat 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.

Contact Details

Adil Rasheed
Klæbuveien 153
Trondheim, Norway

+47-90291771
adil.rasheed@ntnu.no

Education

Swiss Federal Institute of Technology Lausanne

PhD. 2005-2009

Thesis title: Multiscale Modeling of Urban Climate

Indian Institute of Technology Bombay

Master of Technology in Thermal and Fluids Engineering 2000-2005

Thesis: Numerical Modeling of the scavenging process of a two-stroke internal combustion engine

Indian Institute of Technology Bombay

Bachelor of Technology in Mechanical Engineering 2000-2005

Thesis title: Experimental and numerical investigation of the heat transfer characteristics of turbulence generators of different shapes in a square channel.

Work

Department of Engineering Cybernetics, NTNU

Professor, Bigdata Cybernetics 2019 - Present

Combining physics based modeling with data driven machine learning algorithms

Department of Engineering Cybernetics, NTNU

Professor II, Machine Learning and Artificial Intelligence 2018 - 2019

Combining physics based modeling with data driven machine learning algorithms

Mathematics and Cybernetics, SINTEF Digital

Senior Scientist and Research Manager, CSE Group 2012 - Present

My roles in the group in addition to research and development are sales & marketing, PhD supervision and consultancy. Look my CV for details.

University of Tsukuba, Japan

Visiting Scientist June - Aug 2014

Established collaboration with the Center of Computational Sciences. The collaboration gives our group access to the high performance computing resource at the center

Applied Mathematics, SINTEF ICT

Research Scientist 2009 - 2012

IN this role I mostly developed Computational FLuid Dynamics codes and applied them to provide consultancy to Avinor and several architect firms

Induslogic Private Limited

Associate Engineer July - Aug 2005

Projects

EXAIGON, WP Manager and PhD supervision 16MNOK, 2020-2023
Client: RCN, Several industry partners
Theme: Explainable Machine Learning

Autosit, WP Manager and PhD supervision 12MNOK, 2019-2022
Client: RCN, Kongsberg, DNV GL, Marine Robotics
Theme: Autonomous Ship

Lofoten Airport Citing, Investigator,2018-2019
Client: Avinor AS
Citing of a new airport in the Lofoten region
Research partners:KVT, MET

SOLA Building Design, Researcher, 2018-2020
Client: Sømmvågen III AS
Building design optimization

ATB, Initiator and Researcher, 2018-2020
Client: Norwegian Research Council HELSEVEL
Aerial Transport of Biological materials between hospitals
Research partners:FFI, OUS, MET

DIGMOB, Researcher, 2018-2020
Client: Norwegian Research Council TRANSPORT 2025
Digitalization and mobility: Smart and sustainable transport in urban agglomerations
Research partners:TØI

Hybrid Analytics, Project Manager, 2MNOK, 2018
Client: SINTEF Digital
Combining physics based modeling with machine learning algorithms

POP-SEP MLFunc, Researcher, 150kNOK, 2017
Client: SINTEF Predicting the thermoelectric behavior of new chemical molecules using Machine Learning 

E8, Proposal Writing, 100kNOK, 2017
Client: Statens Vegvesen, Towards Designing Intelligent Transport Solution

BIGLEARN, Project Manager, 600kNOK, 2017
Client: SINTEF Serving as one of the four core members of the BIGLEARN initiative within SINTEF designing a roadmap geared towards digitization.

AI-CLIMAMOB, Researcher, 350kNOK, 2017
Client: TØI
Quantify the impact of climate on urban mobility and develop Machine Learning Algorithms to predict number of pedestrians and vehicles on a stretch of road at an instant of time.

ANALYTICS TECHNOLOGY WATCH, Project Manager, 1mNOK, 2017
Client: Kongsberg
Help the client improve and use their Artificial Intelligence and Machine Learning technology platform as a competitive edge in the new age of digitization and design its future value propositions.

ESUSHI, Quality Assurance, 200kNOK, 2016
Client: SINTEF ICT
Analysis of the complicated relationship between meteorological data, oceanographic biomarkers (algae, planktons etc.) and fish catch reports (species caught, equipment used etc.)

E3WDM, Quality Assurance, 300kNOK, 2017
Clustering of time series based on graph weighted by correlation revealing underlying structure. Automatic anomaly detection in sensor data

OPWIND, Work Package Manager, 7mNOK, 2017-2020
Client: Norwegian Research Council, Statoil and Vattenfall
Research Partners: SINTEF Energy, SINTEF DIGITAL, Norwegian Meteorological Institute, NTNU
International Partners: NREL, DTU
Research Area: Real time control system for wind farms, Aerodynamics, Reduced Order Modelling

E39, Task Manager, 600kNOK, Jan 2017-
Client: Norwegian Meteorological Institute, Vegvesen
Research partners: Wind in complex terrain and fjord relevant for bridge designs and road transport.

FSI-WT (Fluid Structure Interaction for Wind Turbine), Work Package Manager
20mNOK, 2012-2016 (www.fsi-wt.no) Client: Norwegian Research Council, Statoil, Trønderenergie, WindSIM, Kjeller Vindteknik
Research Partners: SINTEF ICT, NTNU, FFI, Norwegian Meteorological Institute
Research Area: Fluid Structure Interaction, Aerodynamics, Power Forecasting, Ocean-MET interactions, Wake-terrain-turbine-atmosphere interaction modelling

NOWITECH, Task Manager, 4mNOK, 2009-2017 (www.nowitech.no)
Client: Norwegian Research Council, Statoil, Statkraft, DNV, Dong, CD-ADAPCO
Research Partners: NTNU, SINTEF ICT, SINTEF Energy, MARINTEK, IFE
Research Area: Numerical Methods for Wind Energy, Isogeometric Analysis, Computational Fluid Dynamics, New turbine concepts

URBASIM, Project Manager, 1.5mNOK, Jan-Dec 2016
Client: Norwegian Research Council
Research Area: Wind Energy in an Urban context, Building refurbishment, Air conditioning, Urban Geometry Modelling, Aerodynamic Buildings, 3D printing and scanning, Urban Climatology

DRIFT TURBULENS, Project Manager, 12mNOK, 2010 – (www.ippc.no)
Client: Norwegian Meteorological Institute & Avinor
Research Area: Aviation, Atmospheric turbulence, Microscale wind and turbulence prediction system

SOLA Special Analysis, Project Manager, 600kNOK, Nov 2016 – Client: Sømmvågen III AS
Research Area: Building induced turbulence, Aerodynamic buildings, Architecture

EXTBUILDFLOW, Project Manager, 1.5mNOK, Jan-Dec 2015
Client: Norwegian Research Council
Research Area: Urban Geometry Modelling, Computational Fluid Dynamics, Turbulence Modelling

NEST: Thermal Heat Storage, Quality Assurer, 500kNOK, 2014-2015
Client: NEST
Research Area: Thermal heat storage, thermal stresses induced fracture modeling, design optimization

SESAR (Single European Sky Air traffic management Research), Researcher, 12mNOK, 2010-2016
Client: Euro Control
Research Area: Aircraft wakes, Aviation Safety, Vortex Particle Method

Avinor FoU, Project Manager, 5.2mNOK, 2010-2013
Client: Avinor
Research area: Aviation safety, Atmospheric Turbulence Modelling

BUILDSIM, Project Manager, 275kNOK, Jan-March 2015
Client: FG Eiendom

DAEDALUS, Task Manager, 275kNOK, Jan-Dec 2015
Client: European Space Agency
Research partners: Satavia, Catapult, Avinor ANS, NCAR, UiO
Research area: Atmospheric turbulence, Sensor integration in aircraft

Bodø Special Analysis, Project Manager, 200kNOK, Feb-March 2016

Gimsøya Airport Siting, Project Manager, 300kNOK, Nov 2015-Dec 2016

Sandane Special Analysis, Project Manager, 150kNOK, Dec 2016-

Ørsta Volda Special Analysis, Project Manager, 150kNOK, Dec 2016-

Sola Special Analysis, Project Manager, 300kNOK, Jan-Dec 2015

Flesland Harbour Analysis, Project Manager, 250kNOK, June-Sep 2010

Faroe Island Airport Siting, Project Manager, 250kNOK, 2010-2010

Stokka Airport runway siting, Project Manager, 150kNOK

Kjevik Speial Analysis, Project Manager, 150kNOK

Alta Special Analysis, Project Manager, 150kNOK

Haugesund Special Analysis, Project Manager, 150kNOK

PUBLICATION: JOURNALS

  1. Pawar S, Ahmed SE, San O and Rasheed A. An evolve-then-correct reduced order model for hidden fluid dynamics, Mathematics, 8(4), 570, 2020.
  2. Ahmed SE, San O, Rasheed A and Iliescu T, A long short-term memory embedding for hybrid uplifted reduced order models, Physica D: Nonlinear Phenomena, 409, 132471, 2020.
  3. Havenstrøm ST, Sterud C, Rasheed A, San O, Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles, Download
  4. Meyer E, Robinson H, Rasheed A, San O, Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning, Download
  5. Vaddireddy H, Rasheed A, Staples AE, San O, Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data, Physics of Fluids, 32, 015113, 2020 Download
  6. Pawar S, Ahmed SE, San O and Rasheed A, Data-driven recovery of hidden physics in reduced order modeling of fluid flows, Physics of Fluids, 32, 036602, 2020. , Download
  7. Siddiqui SM, Rasheed A and Kvamsdal T, Validation of the numerical simulations of flow around a scaled-down turbine using experimental data from wind tunnel, Wind and Structures 29, 405-416, 2019
  8. Siddiqui SM, Rasheed A and Kvamsdal T, Numerical assessment of RANS turbulence models for the development of data driven Reduced Order Models, Ocean Engineering, 196, 106799, 2020
  9. Siddiqui SA, Fonn E, Kvamsdal T, Rasheed Astrong>, Finite-Volume High-Fidelity Simulation Combined with Finite-Element-Based Reduced-Order Modeling of Incompressible Flow Problems, Energies. vol. 12 (7), 2019
  10. Pawar S, Ahmed SE, San O and Rasheed A, An evolve-then-correct reduced order model for hidden fluid dynamics, Download
  11. Pawar S, San O, Rasheed A and Vedula P, A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence, To appear in Theoretical and Computational Fluid Dynamics, Download
  12. Sterud C, Robinson H, Rasheed A, Moe S, San O, On the applicability of using a Deep Reinforcement Learning based controller for path following and collision avoidance for autonomous surface vehicles, 2020
  13. Robinson H, Rasheed A, San O, Dissecting Deep Neural Networks, Download
  14. Rasheed A, San O and Kvamsdal T, Digital twin: values, challenges and enablers from a modeling perspective, vol 8, 2020, Download
  15. Ahmed SE, Rahman SM, San O, Rasheed A and Navon IM, Memory embedded non-intrusive reduced order modeling of non-ergodic flows, Physics of Fluids, 31, 126602, 2019. Download
  16. Rahman, S. M., Pawar, S., San, O., Rasheed, A. and Iliescu, T., Nonintrusive reduced order modeling framework for quasigeostrophic turbulence, Physical Review E, 100, 053306, 2019. Download
  17. Siddiqui MS, Kvamsdal T, Rasheed A, High fidelity computational fluid dynamics assessment of wind tunnel turbine test, Journal of Physics: Conference Series, Volume 1356, Number 1
  18. Pawar S, Rahman Sk. M, Vaddireddy H, San O, Rasheed A, and Vedula P, A deep learning enabler for non-intrusive reduced order modeling of fluid flows, Physics of Fluids, 31, 085101,2019 Download
  19. Maulik R, San O, Rasheed A, Vedula P, Data-driven deconvolution for large eddy simulations of Kraichnan turbulence, Physics of Fluids, 30, 125109 (2018)Download
  20. Fonn E, Brummelen H, Kvamsdal T and Rasheed A, Finite Element Divergence-Conforming POD-Galerkin formulation for the development of novel reduced order models, Computer Methods in Applied Mechanics and Engineering, Volume 346, 1 April 2019, Pages 486-512, Download Preprint
  21. Maulik R, San O, Rasheed A, Vedula P, Sub-grid modelling for two-dimensional turbulence using neural networks, Journal of Fluid Mechanics, 858, 122-144, 2019 Download
  22. Rahman SM, San O, and Rasheed A, A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence, Fluids, 3(4), 86, 2018
  23. Rahman SM, Rasheed A and San O, A hybrid analytic framework for accelerating incompressible flow solvers, Fluids 2018, 3(3), 50
  24. Siddiqui MS, Rasheed A, Tabib MV and Kvamsdal T, High fidelity numerical simulation of the aerodynamic characteristics of full scale NREL 5MW wind turbine with varying geometrical complexity: From 2D blade sections to 3D rotating turbine with support structure, Renewable Energy, Volume 132, March 2019, Pages 1058-1075, https://doi.org/10.1016/j.renene.2018.07.06
  25. Tabib MV, Løvvik OM, Johannessen KA, Rasheed A, Sagvolden E, Rusad AM, Discovering thermoelectric materials using Machine Learning, Insights and Challenges, 27th International Conference on Artificial Neural Network 2018
  26. Tabib M, Siddiqui MS, Rasheed A, Kvamsdal T, Industrial scale turbine and associated wake development -comparison of RANS based Actuator Line Vs Sliding Mesh Interface Vs Multiple Reference Frame method, Energy Procedia, Volume 137, October 2017, Pages 487-496, ISSN 1876-6102
  27. Rasheed A, Süld JK, Tabib M, Kvamsdal T, Kristiansen J, Demonstrating the impact of bidirectional coupling on the performance of an ocean-met model, Energy Procedia, Volume 137, October 2017, Pages 443-451, ISSN 1876-6102,
  28. Tabib M, Rasheed A, Siddiqui MS, Kvamsdal T, A full-scale 3D Vs 2.5D Vs 2D analysis of flow pattern and forces for an industrial-scale 5MW NREL reference wind-turbine, Energy Procedia, Volume 137, October 2017, Pages 477-486, ISSN 1876-6102,
  29. Siddiqui MS, Rasheed A, Tabib M, Kvamsdal T, Quasi-Static & Dynamic Numerical Modeling of Full Scale NREL 5MW Wind Turbine, Energy Procedia, Volume 137, October 2017, Pages 460-467, ISSN 1876-6102
  30. Fonn E, Tabib M, Siddiqui MS, Rasheed A, Kvamsdal T, A step towards reduced order modelling of flow characterized by wakes using Proper Orthogonal Decomposition, Energy Procedia, Volume 137, October 2017, Pages 452-459, ISSN 1876-6102
  31. Siddiqui M, Rasheed A, Tabib M, Fonn E, Kvamsdal T. On Interactions Between Wind Turbines and the Marine Boundary Layer. ASME. International Conference on Offshore Mechanics and Arctic Engineering, Volume 10: Ocean Renewable Energy ():V010T09A052. doi:10.1115/OMAE2017-61688.
  32. Fonn E, Rasheed A, Tabib M, Kvamsdal T. A Step Towards a Reduced Order Modelling of Flow Characterized by Wakes Using Proper Orthogonal Decomposition. ASME. International Conference on Offshore Mechanics and Arctic Engineering, Volume 1: Offshore Technology ():V001T01A011. doi:10.1115/OMAE2017-62435.
  33. Tabib M, Rasheed A, Fuchs F. Analysis of Unsteady Hydrodynamics Related to Vortex Induced Vibrations on Bluff-Bodied Offshore Structure. ASME. International Conference on Offshore Mechanics and Arctic Engineering, Volume 2: Prof. Carl Martin Larsen and Dr. Owen Oakley Honoring Symposia on CFD and VIV ():V002T08A027. doi:10.1115/OMAE2017-61207.
  34. Rasheed A, Süld J, Tabib M. Effect of Uni- and Bi-Directional Coupling of Ocean-Met Interaction on Significant Wave Height and Local Wind. ASME. International Conference on Offshore Mechanics and Arctic Engineering, Volume 7B: Ocean Engineering ():V07BT06A052. doi:10.1115/OMAE2017-61681.
  35. Rasheed A, Tabib M, Kristiansen J. Wind Farm Modeling in a Realistic Environment Using a Multiscale Approach. ASME. International Conference on Offshore Mechanics and Arctic Engineering, Volume 10: Ocean Renewable Energy ():V010T09A051. doi:10.1115/OMAE2017-61686.
  36. Mandar V. Tabib, Adil Rasheed, Tanu Priya Uteng, Methodology for assessing cycling comfort during a smart city development, In Energy Procedia, Volume 122, 2017, Pages 361-366, ISSN 1876-6102, https://doi.org/10.1016/j.egypro.2017.07.286.
  37. Tabib M, Siddiqui MS, Fonn E, Rasheed A, Near wake region of an industrial scale wind turbine: comparing LES-ALM with LES-SMI simulations using data mining (POD), Journal of Physics: Conf. Series 854 (2017) 012044
  38. Siddiqui MS, Rasheed A, Tabib M, Kvamsdal T, Influence of Tip Speed Ratio on Wake Flow Characteristics Utilizing Fully Resolved CFD Methodology, Journal of Physics: Conf. Series 854 (2017) 012043
  39. Tabib M, Rasheed A and Fonn E, A computational framework involving CFD and data mining tools for analyzing disease in carotid artery bifurcation, Progress in Applied CFD-CFD2017, ISSN 2387-4295, ISBN 978-82-536-1544-8
  40. Siddiqui M.S, Rasheed A, Tabib M, and Kvamsdal T. "Numerical Modeling Framework for Wind Turbine Analysis & Atmospheric Boundary Layer Interaction", 35th Wind Energy Symposium, AIAA SciTech Forum, (AIAA 2017-1162) http://dx.doi.org/10.2514/6.2017-1162
  41. Nordanger K, Holdahl R, Kvarving AM, Rasheed A, Kvamsdal T, Comparison of three isogeometric incompressible Navier-Stokes solvers applied to simulation of flow past a fixed NACA0012 airfoil, Computer Methods in Applied Mechanics and Engineering, 284, 664-688, 2014
  42. Nordanger K, Holdahl R, Kvamsdal T, Kvarving AM and Rasheed A, Simulation of airflow past a 2D NACA0015 airfoil using an isogeometric incompressible Navier-Stokes solver with the Spalart-Allmaras turbulence model, Computer Methods in Applied Mechanics and Engineering, 290, 183-208, 2015
  43. Siddiqui SM, Rasheed A, Tabib M, Kvamsdal T, Numerical Modeling Framework for Wind Turbine Analysis & Atmospheric Boundary Layer Interaction, To appear in the American Institute of Aeronautics and Astronautics
  44. Tabib M, Rasheed A and Fuchs F, Analyzing complex wake-terrain interactions and its implications on wind-farm performance, Journal of Physics Conf. Series 753 (2016) 032063, doi:10.1088/1742-6596/753/3/032063
  45. Nordanger K, Rasheed A, Okstad KM, Morten A, Holdahl R, Kvamsdal T, Numerical benchmarking of fluid-structure interaction: An isogeometric finite element approach, Ocean Engineering, 124 (324-339), 2016
  46. Fuchs F, Rasheed A and Tabib, Wake modeling in complex terrain using a hybrid Eulerian-Lagrangian Split Solver, Journal of Physics Conf. Series 753 (2016) 082031, doi:10.1088/1742-6596/753/8/082031
  47. Siddiqui MA, Rasheed A, Tabib M and Kvamsdal T, Numerical analysis of the NREL 5MW wind turbine: A study towards a better understanding of Wake Dynamics and Torque Generation mechanism, Journal of Physics Conf. Series 753 (2016), 032059, doi:10.1088/1742-6596/753/3/032059
  48. Siddiqui MS, Rasheed A, Kvamsdal T, Tabib M, Three Dimensional Variable Turbulent Intensity Flow Field Characterization of a Vertical Axis Wind Turbine, Energy Procedia, 80:312-320, 2015
  49. Opstal TV, Fonn E, Kvamsdal T, Kvarving AM, Mathisen KM, Nordanger K, Okstad KM, Rasheed A, Tabib M, Isogeometric methods for CFD and FSI-simulation of flow around turbine blades, Energy Procedia, 80:442-449, 2015
  50. Rasheed A and Mushtaq A, Numerical analysis of the flying condition at the Alta airport, Norway, Aviation, 18(3), 109-119, 2014
  51. Fonn E, Rasheed A, Kvarving AM and Kvamsdal T, Spline based Mesh Generator for high fidelity simulation of flow around turbine blades, Energy Procedia, 80:294-301, 2015
  52. Tabib M and Rasheed A, Investigation of the impact of wakes and stratification on the performance of an onshore wind farm, Energy Procedia, 80:302-311, 2015
  53. Süld JK, Rasheed A, Sætra Ø, Carasco A, Kristiansen J, Kvamsdal T, "Mesoscale Numerical Modelling of Met-Ocean Interactions", Energy Procedia, 80:433-441, 2015
  54. Tabib M, Rasheed A, Kvamsdal T, LES and RANS simulation of onshore Bessaker Wind farm: analyzing terrain and wake effects on wind farm performance, Journal of Physics Conference Series 625 (2015) 012032.
  55. Rasheed A, Holdahl R, Kvamsdal T, Åkervik E, A Comprehensive Simulation Methodology for Fluid-Structure Interaction of Offshore Wind Turbines, Energy Procedia, 53C, pp. 135-145, 2014
  56. Rasheed A, Süld JK, Kvamsdal T, A Multiscale Wind and Power Forecast System for Wind Farms, Energy Procedia, 53C, pp. 290-299, 2014
  57. Rasheed A, Sørli, K, A multiscale turbulence prediction and alert system for airports in hilly regions, Aerospace Conference, 2014 IEEE, 2014, 1-10 , ISBN:978-1-4799-5582-4
  58. Rasheed A, Sørli K, CFD Analysis of Terrain Induced Turbulence at Kristiansand Airport, Kjevik, Aviation, 17, 104-112, 2013
  59. Rasheed A, Sørli K, Holdahl R, Kvamsdal T, A Multiscale Approach to Micrositing of Wind Turbines, Energy Procedia, Volume 14, 1458-1463, 2012
  60. Rasheed A, Robinson D, Characterization of dispersive fluxes in Mesoscale models using LES of flow over an array of cubes, International Journal of Atmospheric Sciences, Article ID 898095, http://dx.doi.org/10.1155/2013/898095 2013
  61. Rasheed A, Robinson D, Clappier A, Narayanan C, Lakehal D, Representing Complexities in Urban Geometry in Mesoscale Modeling, International Journal of Climatology, 289-301, 31, Issue 2, 2011

PUBLICATIONS: BOOK CHAPTERS

  1. Rasheed A, Kvamsdal T, Multiscale Wind Modeling, International Center for Numerical Methods in Engineering (CIMNE), 2014. ISBN 978-84-941686-6-6.
  2. Kvarving AM, Holdahl R, Kvamsdal T, Rasheed A, Parallel computations of air flow around wind turbine blades, International Center for Numerical Methods in Engineering (CIMNE). 2014. ISBN 978-84-941686-6-6.
  3. Book Chapter: The Urban Climate, Computer Modeling for Sustainable Urban Design, Physical Principles. Methods and applications, Earthscan. ISBN 978-1-84407-679-6

PUBLICATIONS: PEER REVIEWED PROCEEDINGS

  1. Rasheed A, Tabib M, Flow characterization in complex terrain Abstract published in the International Journal of Aerospace and Mechanical Engineering Vol:3, No.1, 2016
  2. Shabnam A, Rasheed A, “Investigation of the influence of 5-HT1A R agonist and 5-HT2A/2C R agonist in m-RNA expression of AMPA-R GABA-Aα 1R and BDNF in HT-22 cells of mice”, 4th International Conference and Exhibition on Biometrics and Biostatistics, San Antonio, USA, Abstract published in The Journal of Applied and Computational Mathematics, ISSN: 2168-9679, DOI: 10.4172/2168-9679.C1.002
  3. Fonn E, Rasheed A, Kvarving A, Kvamsdal T, Spline based mesh generator for wind turbine blades, 27th Nordic Seminar on Computational Mechanics, Stockholm, Sweden, 2014
  4. Rasheed A, Süld JK, Kvamsdal T, A Hybrid Numerical and Statistical Model for Wind Power Forecasting, Grand Renewable Energy 2014, International Conference and Exhibition
  5. Rasheed A, Sørli K, Süld JK, Midtbø, Downscaling as a way to predict hazardous conditions for aviation activities, SESAR Innovation Day, 2013
  6. Rasheed A, Robinson D, Lakehal D, On the Effects of Complex Urban Geometries on Mesoscale Modeling, Proceeding of the International Symposium on Computational Wind Engineering 2010, Chapel Hill, North Carolina, USA
  7. Robinson D, Haldi F, Kampf, Leroux P, Perez D, Rasheed A, Wilke U, From the Neighborhood to the city: Resource Flow Modeling for Urban Sustainability, Proceeding of the CISBAT 2009, Lausanne, Switzerland
  8. Rasheed A, Robinson D, Narayanan C, Lakehal D, On the Effects of Complex Urban Geometries on Mesoscale Modeling, Proceeding of the seventh International Conference on Urban Climate 2009, Yokohama, Japan
  9. Rasheed A, Robinson D, Clappier A, A New Urban Canopy Model, Proceeding of the seventh International Conference on Urban Climate 2009, Yokohama, Japan
  10. Rasheed A, Robinson D, Multiscale Modeling of Urban Climate, Proceeding of the Eleventh International IBPSA Conference: Building Simulation 2009, Glasgow, UK
  11. Robinson D, Haldi F, Kämpf, Leroux P, Perez D, Rasheed A, Wilke U, CITYSIM: Comprehensive Micro Simulation of Resource Flows for Sustainable Urban Planning Proceeding of the Eleventh International IBPSA Conference: Building Simulation 2009, Glasgow, UK
  12. Rasheed A, Robinson D, Clappier A, On the sensitivity of Building Performance to the Urban Heat Island Effect, Proceeding of the CISBAT 2007, Lausanne, Switzerland

PUBLICATION: POSTERS

  1. Fonn E, Rasheed A, Tabib M, Kvamsdal T, Siddiqui MS, Kvarving AM, Okstad KM, Torturing data with artificial intelligence, Departmental meet 2017
  2. Rasheed A, Tabib M, Fonn E, Kvamsdal T, Siddiqui MS, Kvarving AM, Fuchs F, Okstad KM, Gone with the wind, Departmental meet 2017
  3. Kvamsdal T, Johannessen KA, Kvarving AM, Okstad KM, Fonn E, Rasheed A, Tabib M, AFES: Autonomous Finite Element Simulation, Departmental meet 2017
  4. Kvarving AM, Kvamsdal T, Rasheed A, Okstad KM, Fonn E, Mathisen KM, Nordanger K, Opstal Tv, Tabib M, 3D CFD and FSI-simulation of flow around turbine blades, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  5. Okstad KM, Mathisen KM, Kvamsdal T, Kvarving AM, Nordanger K, Rasheed A, Tabib M, Fonn E, Opstal Tv, 3D Beam element for FSI-simulation of flow around turbine blades, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  6. Nordanger K, Kvamsdal T, Kvarving AM, Mathisen KM, Okstad KM, Rasheed A, Fonn E, Opstal Tv, Tabib M, Strip theory approach for FSI-simulation of flow around turbine blades, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  7. Siddiqui MS, Rasheed A, Kvamsdal T, Tabib M, Three Dimensional Variable Turbulent Intensity Flow Field Characterization of a Vertical Axis Wind Turbine, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  8. Opstal TV, Fonn E, Kvamsdal T, Kvarving AM, Mathisen KM, Nordanger K, Okstad KM, Rasheed A, Tabib M, Isogeometric methods for CFD and FSI-simulation of flow around turbine blades, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  9. Fonn E, Rasheed A, Kvarving AM and Kvamsdal T, Spline based Mesh Generator for high fidelity simulation of flow around turbine blades, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  10. Tabib M and Rasheed A, Investigation of the impact of wakes and stratification on the performance of an onshore wind farm, 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  11. Mushtaq A, Rasheed A, Kvamsdal T, Tabib M, "Statistical Analysis of wind mast data from the Bessaker Wind Farm", 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  12. Süld JK, Rasheed A, Sætra Ø, Carasco A, Kristiansen J, Kvamsdal T, "Mesoscale Numerical Modelling of Met-Ocean Interactions", 12th Deep Sea Offshore Wind R&D Conference, Deepwind 2015
  13. Süld JK, Midtbø KH, Kristiansen J, Rasheed A, Kvamsdal T, Wind turbine power production forecasting and design methodology, The World Weather Open Science Conference, Montreal, Canada, 2014
  14. Nordanger K, Kvamsdal T, Holdahl R, Kvarving AM, Rasheed A, Simulation of flow past a NACA0015 airfoil using an isogeometric incompressible Navier-Stokes solver, NOWITECH Day, Trondheim 2014
  15. Åkervik E, Rasheed A, Holdahl R, FSI: Fluid Solid Interaction for Wind Turbine, Poster presentation in the 11th Deep Sea Offshore Wind R&D Conference, Deep Wind 2014
  16. Rasheed A, Süld JK, Kvamsdal T, A Multiscale Wind and Power Forecast System for Wind Farms, Poster presentation in the 11th Deep Sea Offshore Wind R&D Conference, Deep Wind 2014
  17. Rasheed A, Holdahl R, Kvarving AM, Süld JK, Fluid Structure Interaction for Wind Turbines", NOWITECH Day, Trondheim, 2013

PUBLICATION: ORAL PRESENTATIONS

  1. Rahman Sk.M, Rasheed A, San O, A Hybrid Analytics Paradigm Combining Physics-Based Modeling and Data-Driven Modeling to Accelerate Incompressible Flow Solvers, 71st Annual Meeting of the APS Division of Fluid Dynamics Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
  2. Maulik R, Rasheed A, San O, Data-driven deconvolution for the large eddy simulation of Kraichnan turbulence, 71st Annual Meeting of the APS Division of Fluid Dynamics Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
  3. Tabib M, Rasheed A, Use of hybrid analytics methods to decide on drone landing and take-off platforms in urban areas, Drone Conference Hamar, 19th June 2018
  4. Johannessen KJ, Muntingh G, Rasheed A, Kvamsdal T, On the use of Convolutional Neural Network to accelerate isogeometric analysis, ECCM, ECFD VVII Conference 2018, Glasgow
  5. Kvamsdal T, Fonn E, van Brummelen EH, Rasheed A, Reduced Order Models for Divergence-Conforming Isogeometric Flow Simulations, WCCM XIII, The 13th World Congress on Computational Mechanics, New York, USA
  6. Fonn A, Brummelen EH van, Kvamsdal T, Rasheed A, Siddiqui MS, Fast Divergence-Conforming Reduced Basis Methods for Steady Navier-Stokes Flow, IGAA 2018, Amsterdam, Netherlands
  7. Rasheed A, Hybrid Analytics – Trends and forward looking, Kongsberg Technology Forum, 2017, Sundvollen
  8. Fonn A, Brummelen EH van, Kvamsdal T, Rasheed A, Spline-based Compatible Reduced Basis Methods for Flow Problems, IGA 2017. Pavia, Italy
  9. Rasheed A, Empirical Model Decomposition based mathematical modelling strategy for analyzing Actigraphy Data and correlating it to Clinical Psychiatric Evaluation, Actigraphy Conference, Trondheim, Norway, 2016
  10. Kvarving AM, Kvamsdal K, Okstad KM, Mathisen KM, Fonn E, Johannessen KA, Rasheed A and Holdahl R, IFEM - an isogeometric toolbox for the solution of PDEs, III International Conference on Isogeometric Analysis, June 2015, Trondheim, Norway, ISBN : 978-84-943928-5-6
  11. Tabib M, Rasheed A, Kvamsdal T, Simulation of the on-shore Bessaker wind farm: Analyzing terrain and wake effects on the wind farm performance, Wake Conference, 2015
  12. Opstal Tv, Fonn E, Kvamsdal T, Kvarving AM, Mathisen KM, Nordanger K, Okstad KM, Rasheed A, and Tabib M, FSI of wind turbine blades, VI International Conference on Coupled Problems in Science and Engineering, Venice, 2015
  13. Kvamsdal T, Fonn E, Kvarving AM, Mathisen KM, Nordanger K, Okstad KM, Opstal Tv, Adil Rasheed, and Mandar Tabib, Strip theory approach for FSI of offshore wind turbine blades, VI International Conference on Coupled Problems in Science and Engineering, Venice, 2015
  14. Rasheed A, Sørli K, Kvamsdal T, Application of a Multiscale Turbulence Prediction System for aviation safety and wind turbine siting, 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Austria, September 10-14, 2012
  15. Rasheed A, Sørli K, Ødegaard V, Kvamsdal T, Midtbø H, Potential of Numerical Turbulence Prediction System for Wind Turbine Micrositing, International Conference on Renewable Energy Utilization, Coimbatore, India, 2012
  16. Kvarving AM, Holdahl R, Kvamsdal T, Rasheed A, Isogeometric 2-D CFD Simulations of Turbulent Flow around Bluff Bodies, US National Conference of Computational Mechanics, 2011
  17. Rasheed A, Sørli K, Holdahl R, Kvamsdal T, A Multiscale Approach to Micrositing of Wind Turbines, International Conference on Advances in Energy Engineering, Bangkok 2011
  18. Rasheed A, Multiscale Modelling of Urban Climate, 1st Swiss Building and Urban Simulation Conference, Lucerne, Switzerland 2009
  19. Rasheed A, Nandi K, Date AW, A Novel Experimental Set-up for Investigation of Perfect Mixing and Perfect Displacement Models for the scavenging process in a Cavity, 7th World Conference on Experimental Heat Transfer, Fluid Mechanics and Thermodynamics 2009, Krakow, Poland
  20. Blond N, Belalcazar LC, Rasheed A, Clappier A, Huttner S, Bruse M, Design and Test of a System to Simulate Road Traffic Emissions, Annales de ILSUP 2008
  21. Rasheed A, Robinson D, Clappier A, Investigation of the nature of the dispersive fluxes in Urban Parametrization using Large Eddy Simulation, 2008, Annual Meeting, Boston, Massachusetts
  22. Robinson D, Filchakova N, Kaempf J, Rasheed A, Scartezzini JL, Towards an evolutionary model of city sustainability in Holcim Forum, Beijing, 2007

PUBLICATIONS: SCIENTIFIC REPORTS

  1. Tabib M and Rasheed A, Machine Learning based investigation of influence of weather on transport mobility. SINTEF Report
  2. Rasheed A, Tabib M and Midtbø KH, Evaluating the terrain induced wind and turbulence conditions at the Ørsta Volda Airport to design approach and departure trajectories. SINTEF Report F????
  3. Rasheed A, Tabib M and Midtbø KH, Evaluating the terrain induced wind and turbulence conditions at the Sandane Airport to design approach and departure trajectories. SINTEF Report F????
  4. Rasheed A, Tabib M, Phase 2+3: Assessment of the impact of a proposed building on a the flying condition on the runways at the Stavanger Airport, Sola using numerical simulations. SINTEF Report F????
  5. Rasheed A, Tabib M, Phase 1: Assessment of the impact of a proposed building on a the flying condition on the runways at the Stavanger Airport, Sola using numerical simulations. SINTEF Report F????
  6. Rasheed A, Tabib M and Franz Fuchs, Wake Vortex Micro-Scale Turbulence Prototype Development and Sensitivity Studies, Contribution to SESAR 12.2.2 report
  7. Tabib M, Fuchs F and Rasheed A, Report on wake vortex micro-scale turbulent airflow interaction methodologies, Contribution to SESAR 12.2.2 report
  8. Rasheed A, Fonn E and Tabib M, Evaluaring av flyforhold på Gimsøya for en potensiell flyplass, March 2016, SINTEF REPORT: 27374
  9. Rasheed A and Tabib M, Investigation of the impact of a proposed building on the flying conditions for helicopters inside the Bodø airport, March 2016
  10. Sørli K, Tabib M, Rasheed A, Analysis of Turbulent Wakes behind Helicopter Hangers at Sola airport, June 2015, SINTEF Report: A27048
  11. Tabib M, Rasheed A, Impact analysis of the proposed parking lot on the flying condition close to the Kristiansund airport, May 2015, SINTEF Report: F26978
  12. Rasheed A, Tabib M, Investigation of flying conditions at the Stavanger airport, Sola using numerical simulations, October 2014 SINTEF Report F26399
  13. Rasheed A, Sørli K, Analysis of Terrain-induced Turbulence at Bergen Airport Flesland in Connection with new locations for Bergen Harbor: A qualitative analysis, April 2013 SINTEF Report
  14. Sørli K, Rasheed A, Analysis and Siting of a New Runway at the Sandnessjoen Airport, Stokka with respect to mountain induced turbulence, September 2011 SINTEF Report F20557
  15. Sørli K, Rasheed A Terrain-forced Wind and Turbulence and Optimization of Local Domains and Grids for Terrain-induced Turbulence Forecast, July 2011 SINTEF Report F19928
  16. Rasheed A, Sørli K, Midtbø KH, Analysis of Terrain-Induced Turbulence on Alternative Airport Locations at the Faroe Islands using Numerical Simulation, July 2011 SINTEF Report F19941
  17. Rasheed A, Sørli K, Methods of improving the system for terrain induced turbulence forecast at Norwegian Airports, December 2010 SINTEF Report F17589
  18. Rasheed A, Sørli K, Evaluation of the Impact of the Construction of a New Harbor near Flesland Airport in Bergen, September 2010 SINTEF Report F16472
  19. Rasheed A, Sørli K, Estimation of the Terrain Induced Turbulence on the Alta Airport using Numerical Simulations, July 2010 SINTEF Report F16291
  20. Rasheed A, Sørli K, Estimation of the Terrain Induced Turbulence on the Haugesund Airport using Numerical Simulations, July 2010 SINTEF Report F16290
  21. Rasheed A, Sørli K, CFD analysis of Terrain Induced Turbulence at Kristiansand Airport Kjevik, April 2010 SINTEF Report F15622
  22. Final Scientific Report for Swiss National Science Foundation: Multiscale Modeling of Building Urban Interactions NRP 54 Sustainable Development of the Built Environment
  23. PhD Thesis: Multiscale Modeling of Urban Climate, EPFL, Lausanne, Switzerland
  24. Masters Thesis: A Numerical and Experimental Study of the Scavenging Process of a Two-Stroke IC Engine, Mechanical Engineering Department, Indian Institute of Technology Bombay 2005
  25. Bachelors Thesis: Evaluation of the performance of various Heat Transfer Enhancement Devices in a Square Channel, Mechanical Engineering Department, Indian Institute of Technology Bombay 2004

INVITED TALKS

  1. Industry performance enhanced by hybrid modelling, Invited talk at the SINTEF Petroleum Conference 2018
  2. Wind Energy Modelling from a Wind Farm to a Wind Blade Scale, Invited Talk at the Tsukuba Science Week 2016
  3. Flow modeling and pollutant transport in an urban area, 2015, Trondheim Kommune,
  4. Dealing with the complexities in urban geometry in Mesoscale Modeling of Urban Climate, 2014, Meteorology and Climatology Group, University of Tsukuba, Japan,
  5. Multiscale Wind and Temperature Modeling, Plenary talk at the Parallel CFD 2014 Conference, Trondheim Norway
  6. A Multiscale Approach to Micrositing of Wind Turbines, Kongsberg, Trondheim, Norway
  7. First set of results from the Wind forecast model for the Bessaker Wind Farm, Wind SIM, 2012, Tønsberg
  8. Norwegian Offshore Wind Technology: NOWITECH project, Indo-Norwegian workshop on Renewable Energy, Mumbai 2013, India
  9. Microscale CFD model for Wind Energy Forecast in a complex terrain, National Center for Atmospheric Research, Boulder, Colorado, 2012 USA
  10. A Multiscale Approach to Micrositing of Wind Turbines, Trønderenergie, Trondheim, Norway
  11. Recent Developments in Turbulence Modeling, Norwegian Meteorological Institute, Oslo, Norway
  12. Multiscale Modeling of Urban Climate, Department of Engineering, University of Cambridge, Cambridge 2009, UK

Artificial Intelligence and Machine Learning

Advanced Machine Learning and AI algorithms are being used to solve problems from a very diverse domain. At the moment we mostly used supervised (Linear and Logistic Regression, Decision Tree and Random Forest, Support Vector Machine, Deep Neural Networks, Convlutional Neural Network, Recurrent Neural Network, Generative Adversarial Networks) and unsupervised (K-neares neighbor, Self Organized Maps, Autoencoders) learning algorithms. Some of the application areas have been wind power forecasting, discovering new chemical compunds with desired properties, predicting pedestrian and bikers behavior in cities and identifying favourable locations for fishing. We have also used anomaly detection algorithms to identify malfunctioning sensors in a system. Recently I conducted a course on "Practical Machine Learning" at the Geilo Winter School. It included interactive python sessions. The school was attended by 80 researchers from industry and academia. I am also designing a 15 minutes weekly online course for SINTEF employees.

Aviation

In cooperation with the Norwegian Meteorological Institute we have developed a microscale terrain-induced wind and turbulence alert system. The system is operation for 20 Norwegian airports and provide realtime forecast of wind and turbulence. We are also involved in siting of airports, runways and buildings close to the airports. During our involvement in the SESAR project, we also developed a computationally efficient aircraft wake-vortex model which has all the physics required for the initiation, transport and diffusion of the vortices.

Building Physics

My research in this topic dates back to the time when I was working on my PhD. During that time I conceived and implemented the concept of equivalent geometry whihc enables accounting for the complexity in urban geometry in a mesocale model for atmospheric flows. In particular I developed models for simulating wind, solar radiation and occupants behavior to model urban climate more accurately. Although most of the work was at an urban scale it is trivial to apply the concepts and tools to individual buildings.

Computational Fluid Dynamics

Over the years my group has developed a set of tools which enable us to simulation highly complicated flows using the national supercomputing infrastructure. In particular, we can simulate incompressible turbulent and multiphase flows dominated by large variety of spatio-temporal scales. We develop solvers from scratch and at the same time we build upon the opensource OpenFoam solvers. The latter enables us to solve industrial scale problems. So far we have applied CFD in the field of meteorology, chemical reactor modeling, wind energy.

Creative Art

I n my personal capacity I have always been using painting, photographs and other such illustration to get a better insight ino the working of "black box" machine learning algorithms. I have also been effectively using art to explain complex topics to general public.

Hybrid Analysis and Modeling

We define "Hybrid Analysis and Modeling" as a modeling technique 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. We have just acquired a project to test out some ideas which are quite promising.

Wind Energy

We have been involved in several wind energy prjects and we practically do almost everything that can be numericaly simulated. Using our tools and expertize we can simulate anything starting from the flow aorund a 2D blade to the windfarm-windfarm interactions. Currently, our models are in operational use for forecasting wind energy from an onshore wind farm.

Pre-project and masters project topics for 2020-21


Some things worth knowing:
  1. Tht titles and the project description can be adapted to the interest and skills of the students
  2. Although some project might sound very challenging, there might be some ground work already in place so just get in touch to know more
  3. I prefer that the students use skype to interact with me as and when required, overeaf to share their writing work (reports and articles) and github for sharing the codes
  4. For physical meetings just drop a message on skype to find my availability and drop by my office

Sample of pre-projects from 2019-20
  1. Meyer, E.: Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
  2. Herman Stavelin: Marine life through You Only Look Once's perspective
  3. Simen Havenstrom: Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles
  4. Duy Tan Tran: GANs enabled super-resolution reconstruction of wind field
  5. Camilla Sterud: Deep Reinforcement Learning for Path Following and Collision Avoidance, 5th Norwegian Big Data Symposium
  6. Haakon Robinson: Dissecting deep neural networks

Project 1: Ruteplanlegging for autonome farkoster
Background: The ability to route planning is central to autonomous vehicles. Based on a world model, the vessel calculates optimal routes depending on the situation and mission. Routes must be planned both over greater distances, based on map data and other advance information, and in the immediate area based on data from the vehicle sensors combined with the map data. We work with route planning for both ground vehicles and boats. Some issues are relevant for both types of craft, while other issues are more specific.

In order to do multi-level route planning, hierarchical route planning is required to reuse results at a higher level when planning more detailed routes. In addition to avoiding obstacles, it is important to map the terrain so that the vehicle can choose routes through low-risk areas. This work includes automatic analysis of data on the surroundings, such as weather and terrain, to determine how suitable different areas are for different tasks.

For some types of missions it is necessary to move in hiding, other times you will move quickly or observe certain areas. Different terrain characteristics are preferred for the different tasks, and we want to develop methods for calculating this automatically. There is also a need to further develop existing optimization methods to search for routes.

There is room for several tasks within this theme. All the thesis is intended as project assignments of 15 credits, and is suitable for extension to a master's thesis. We note that FFI requires the grade B or better on average to write a master's thesis for us.

Problem Statement: Dynamic environment: The environment in which an autonomous vehicle moves will often be dynamic, so the cost of a move will depend on when the move occurs. Examples of this may be dynamic obstacles, phenomena such as queuing, need to stay close to another unit along the way, or varying weather conditions. In many cases, it is possible to estimate future changes, so that this can be taken into account in route planning. This is a challenge for traditional methods such as Dijkstra, and it is no longer certain that existing methods provide optimal routes. The task is intended to be based on graph-based route planning algorithms, such as Dijkstra, A * / D * or other. However, the student is free to come up with their own suggestions for solutions.

Contact at NTNU: Adil Rasheed (adil.rasheed@ntnu.no)
Contacts at FFI: Martin Syre Wiig (martin-syre.wiig@ffi.no)

References
  1. Meyer E., Robinson, H., Rasheed, A., and San, O., Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning, IEEE Access, 8, 41466-41481, 2020
  2. Meyer E, Heibergh A, Rasheed A and San O, COREG-compliant path following and collision avoidance with moving obstacles in the Trondheim fjord using Deep Reinforcement Learning, In preparation
  3. Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles

Project 2: Medical Diagnosis
How can we give artificial intelligence systems used for medical diagnosis the ability to say... "I have never seen this before. Don't trust me on this one." ?

Problem Description: Medical diagnosis is difficult because not every case is a typical case. Sometimes patients have signs and symptoms that are unusual. In extreme cases, due to genetic mutation, the patient is the only person in the world with the disease and its associated symptoms.

When a doctor comes across an unusual case she may say "I've never seen this before." and proceed carefully. An AI system that diagnoses patients should also have the ability to say... "I have never seen this before. Don't trust me on this one." Assuming the AI system is based on supervised learning, how can we add capability to the AI system so that it tells users to what extent its training dataset is representative of the current case? Maybe this requires a secondary AI algorithm built with unsupervised learning?

We can attempt to tackle this problem by looking at a system which performs automatic analysis of lung sounds. Such a system could perform better in the real-world if it detected input data which will perform poorly due to either the input data not being representative of the dataset or the data is of poor quality. The excluded data can then be assessed through traditional means such as review by a doctor.

Company dedeX is a med tech startup. dedeX is looking to solve the problem: how do we get the data needed to build AI algorithms that can empower medical history taking and physical examination? dedeX understands both real-world healthcare and AI and can give the student feedback from this unique perspective.

Data: Two separate datasets of annotated lung sounds with different bias and variance should be used. One is the ‘development’ dataset and the other is a ‘real-world’ dataset. The development dataset will be used to create a ‘primary’ supervised machine learning algorithm and a ‘secondary’ unsupervised machine learning algorithm.

The primary algorithm will be used to classify lung sounds in both datasets. This algorithm can be based on existing work by other researchers.

The secondary algorithm will identify data from the real-world dataset that will perform poorly when used in the primary algorithm. In simplified terms it is an unsupervised learning algorithm which looks for the data that is the least similar to data in the development dataset. When this data is excluded from the real-world dataset then the primary algorithm should achieve higher accuracy on the real-world dataset.

We have identified four sources of annotated lung sound data. The student can pick two of these for this project or alternatively find other data sources if more convenient.

  1. Int. Conf. on Biomedical Health Informatics - ICBHI 2017 http://www.auditory.org/mhonarc/2018/msg00007.html
    https://www.kaggle.com/vbookshelf/respiratory-sound-database
  2. Department of Community Medicine at UiT – The Arctic University of Norway
    http://bdps.cs.uit.no
    "In the Tromsø Lung Sounds project we are building a database with more than 36.000 lung sound recordings. The recordings are done as part of Tromsøundersøkelsen 7, which is an Epidemiological study that was started in 1974. The database will be used to provide educational and analysis services for lung sounds. Our contributions are methods for automated classification and similarity search for the sounds. This project is done in collaboration with Hasse Melbye at the Department of Community Medicine, University of Tromsø. The results from this project are further developed by our Medsensio AS startup.”
  3. Thinklabs
    https://www.thinklabs.com/lung-sounds
  4. Interactive Systems for Healthcare
    https://is4health.com

Task

  1. Investigate the possibility of applying existing state-of-the-art deep learning methods to solve the above tasks. As part of this, the student is expected to perform a state-of-the-art literature review and implement the most relevant method(s) that can solve the problem.
  2. Make the chosen method time effective, feasible and available for researchers or developers of AI technologies for diagnosis.

Thesis Information
Timeframe: 6 or 12 months

Contact at NTNU: Adil Rasheed (adil.rasheed@ntnu.no)
Contacts at dedeX: Jon Bekker(jon.floystad@dedex.ai)


Project 3: Alternate representation of Deep Neural Networks (Affiliated to the Explainable AI project EXAIGON)
Neural networks have been applied to a vast array of problems, many of them safety critical. Despite their usefulness, there are many unanswered questions regarding their robustness, stability, and overall safety. This is problematic, as they are increasingly being applied to robotics and control problems in research.

A surprising fact about neural networks is that if they only use piecewise linear activation functions (for example, ReLU), the network as a whole will be piecewise linear There is a vast literature on this class of functions, which can be leveraged in the study of neural networks. A central challenge with this approach is that the functions are extremely complicated, with huge numbers of linear regions. Despite this, these ideas have been used to:

  1. Verify stability/robustness properties of networks
  2. Locally “patch” neural networks using MILP optimisation
  3. Visualise the internal structure of neural networks during and after training

This is a cutting-edge field within deep learning, with many possible applications and future research directions. The following research projects are available as thesis topics:

  1. Model predictive control (MPC) is a flexible control framework that provides stability guarantees while respecting system constraints. The work will assess the feasibility of applying explicit MPC methods to piecewise linear neural networks.
  2. Neural network patching is a relatively unexplored method to locally adjust the output of a piecewise linear neural network using optimisation. The work will involve investigate how patching can be used in the process of machine learning controller design and/or system identification, as well as how to use piecewise linear methods to identify and diagnose issues in controller output. There is potential for collaboration with other students here.
  3. Computing the piecewise linear regions of a neural network is both computationally and memory intensive due to the large number of regions. However, the computations are very parallelisable, and neural networks contain a lot of structure that can be utilised. This project will involve developing efficient parallel algortihms to solve this problem (possibly using GPU programming) and compact data structures to represent the network and speed up later queries on the model.

Desired background:

  1. Strong programming skills in Python and MATLAB (C and C++ are also an advantage)
  2. Basic knowledge of machine learning, in particular neural networks
  3. Good grasp on optimisation

Relevant bibliography:

  1. Robinson, Haakon; Rasheed, Adil; San, Omer. (2019) Dissecting Deep Neural Networks. arXiv.org

Contact at NTNU: Adil Rasheed (adil.rasheed@ntnu.no)
Co-supervisor: Haakon Robinson (haakon.robinson@ntnu.no)


Project 4: Machine Learning Controllers

Problem Description: There exists a multitude of traditional controllers to determine the control inputs (actuators) to achieve the desired behavior of a dynamic system, e.g. feedback linearizing, backstepping, sliding mode, adaptive control etc. However, these controllers are often based on a dynamic model of the system, numeric values for system parameters and several assumptions which are not always accurate. By applying machine learning, it is possible to develop controllers that are not based on a model or assumptions. Specifically, Machine Learning Control (MLC) aims to learn an effective control law b = K(s) that maps the system output (sensors s) to the system input (actuators b) and can solve problems involving complex control tasks where it may be difficult or impossible to model the system and develop a useful control law. The pre-project phase of the project will involve:

  1. Set up a simulation environment for some system (drones, ships, AUVs, etc.)
  2. Implement some of the traditional controllers

The pre-project will lead to a masters project where the major steps will be as follows:

  1. Set up a simulation environment for some system (drones, ships, AUVs, etc.)
  2. Implement some of the traditional controllers
  3. Use some MLC-approaches (regression problem, Actor-Critic reinforcement learning) to develop new controllers
  4. Test and compare performance in case of
    1. Perfect model knowledge
    2. Errors in model parameters
    3. Noisy measurements
    4. Unmodeled nonlinearities
  5. Investigate stability properties/robustness/convergence of ML-controllers

Supervisor: Adil Rasheed (adil.rasheed@ntnu.no), NTNU
Co-supervisor: Haakon Robinson, NTNU (haakon.robinson@ntnu.no)

Reference

  1. Meyer, E., Robinson, H., Rasheed, A., and San, O., Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning, IEEE Access, 8, 41466-41481, 2020.
  2. Sterud, Camilla; Robinson, Haakon; Rasheed, Adil; Moe, Signe; San, Omer. Deep Reinforcement Learning for Path Following and Collision Avoidance. 5th Norwegian Big Data Symposium; 2019-11-13

  3. Project 5: Evaluating the hydrostatic characteristics of a ferry using high fidelity simulations and dimensionality reduction algorithms

    Project Description:

    Airflow around a typical ferry is dominated by a large variety of spatio-temporal scales and complex flow structures. State-of-the art Computational Fluid Dynamics simulations have been used to simulate the drag, yaw and pitch characteristics of such ferries. A solid body like a ferry has intricate designs all of which are not relevent for evaluating its hydrodynamic characteristics. To this end, in this project the student will evaluate and quantify the impact of geometric simplifications of a ferry on its hydrodynamic characterists. The flow simulator based on OpenFoam will be utilized to generate the 3D wind and turbulence field around a moving ferry. The results will be postprocessed using advaced dimensionality reduction algorithms like Proper Orthogonal Decomposition and AutoEncoders. The project will include the following tasks:

    1. Developing a detailed water tight geometry of the ferry in STL format
    2. Simplification / approximation of the geometry
    3. Generation of computational mesh around the solid geometry
    4. Conducting a set of high fidelity simulations for different wind conditions
    5. Go to step 2 and try a different simplification strategy
    6. Analyze the data using POD and AE

    Supervisor: Adil Rasheed, NTNU
    Co-supervisor: Mandar Tabib, SINTEF Digital


    Project 6: Explaining turbulence using Deep Learning (Affiliated to the Explainable AI project EXAIGON)
    Turbulent flows are dominated by a large variety of spatio-temporal scales. So far tools like Fast Fourier Transform and Wavelet Transform have proven to be the workhorse for analyzing these scales. With the recent revolution in Deep Learning and more particularly in Convolutional Neural Network (CNN) and Auto Encoders (AE) we are witnessing huge improvements in classification and data compression tasks. It is foreseen that the automatic pattern identification properties of CNN and important feature identification properties of AE can be utilized to improve our understanding of physical phenomena governed by multiple spatio-temporal scales like turbulence. In this project the focus will be to better describe flow structures observed in turbulent flows. The pre-project will involve the following tasks:

    1. Develop a good understanding of CNN and AE
    2. An intercomparion of Auto Encoders and Principal Component Analysis
    3. Evaluation of AE as an effective feature detector

    The pre-project will lead to a Masters project which will have the following steps:

    1. Data generation: Numerical simulation of turbulent flows will be conducted using softwares developed at SINTEF Digital
    2. Development and training of CNN and AE on turbulent flows simulation data
    3. Using the trained algorithms to analyze the flow structures in turbulent flows

    Supervisor: Adil Rasheed, NTNU
    Co-supervisor: Mandar Tabib and Kjetil Andre Johannessen, SINTEF Digital
    Learning outcome: Computational Fluid Dynamics, Turbulence Modeling, Deep Learning, Dimensionality Reduction


    Project 7: Hybrid Analysis and Modeling as an enabler for Big Data Cybernetics

    Project Description: Most of the modeling approaches lie in either of the two categories:

    Physics based modelling:strong> So far, the engineering community has been driven mostly by a physics-based modeling approach. This approach consists of observing a physical phenomenon of interest, developing a partial understanding of it, putting the understanding in the form of mathematical equations and ultimately solving them. Due to partial understanding and numerous assumptions along the line from observation of a phenomena to solution of the equations, one ends up ignoring a big chunk of the physics. High-performing simulators capable of handling a billion degrees of freedom are opening new vistas in simulation-based science and engineering and combined with multiscale modeling techniques have improved significantly the predictive capabilities. Thus, the dream of establishing numerical laboratories (e.g. numerical wind tunnels) can now be realized for many interesting real world systems. However, despite their immense success, the use of high-fidelity simulator has so far been limited to the design phase. Some of the advantages of physics based models are that they have robust foundation from first principle, are trustworthy, are generalizable to new problems and robust theory exists for uncertainty and error analysis of the model results. However, these models are generally computationally expensive and not adapting to new scenarios automatically.

    Data-driven modeling: While physics based models are the workhorse at the design phase, with the abundant supply of big data, opensource cutting edge and easy-to-use libraries (tensorflow, torch, openAI), cheap computational infrastructure (CPU, GPU and TPU) and high quality, readily available training resources, data-driven modeling is becoming very popular. Compared to the physics based modeling approach, this approach is based on the assumption that since data is a manifestation of both known and unknown physics, by developing a data-driven model, one can account for the full physics. The data-driven approach in general and deep learning in particular has started achieving human level performance in several tasks which were until recently considered impossible for computers. Notable among these are image classification, creative art, anomaly detection. SOme of the advantages of these models are that they can learn in real time from the data, can be highly accurate and are generally very computationally efficient making them idle for real time control applications. However, owing to their blackbox nature, poor generalizability and no robust theory for uncertainty and error quantification, their acceptability in safety critical autonomous systems is limited. Thus for a dream model we have identified some characteristics which are as follows:

    1. Computational efficiency
    2. Intelligence to model the unknown
    3. Evolution and self adaptation of models
    4. Interpretability of models

    The four desired model characteristics mentioned above can be addressed by developing a new breed of modelling approach that will combine 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. We call this new approach as Hybrid Analysis and Modeling.

    Supervisor: Adil Rasheed, NTNU
    Co-supervisor: Damiano Varagnolo, Trond Kvamsdal, NTNU
    Learning outcome: Hybrid Analysis and Modeling paradigm in modeling

    Reference

    1. Pawar, S., Ahmed, S. E. , San, O., and Rasheed, A. An evolve-then-correct reduced order model for hidden fluid dynamics, Mathematics, 8(4), 570, 2020.
    2. Ahmed, S. E. , San, O., Rasheed, A., and Iliescu, T. A long short-term memory embedding for hybrid uplifted reduced order models, Physica D: Nonlinear Phenomena, 409, 132471, 2020.
    3. Pawar, S., Ahmed, S. E. , San, O., and Rasheed, A. Data-driven recovery of hidden physics in reduced order modeling of fluid flows, Physics of Fluids, 32, 036602, 2020.
    4. Rasheed, A., San, O., and Kvamsdal, T. Digital twin: values, challenges and enablers from a modeling perspective, IEEE Access, 8, 21980-22012, 2020.
    5. Pawar, S. , San, O., Rasheed, A. and Vedula, P. A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence, Theoretical and Computational Fluid Dynamics, accepted, 2020.
    6. Vaddireddy, H., Rasheed, A., Staples, A. E. and San, O. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data, Physics of Fluids, 32, 015113, 2020.
    7. Ahmed, S. E., Rahman, S. M., San, O., Rasheed, A. and Navon, I. M. Memory embedded non-intrusive reduced order modeling of non-ergodic flows, Physics of Fluids, 31, 126602, 2019.
    8. Rahman, S. M., Pawar, S., San, O., Rasheed, A. and Iliescu, T. Nonintrusive reduced order modeling framework for quasigeostrophic turbulence, Physical Review E, 100, 053306, 2019.
    9. Pawar, S., Rahman, S. M., Vaddireddy, H. , San, O., Rasheed, A. and Vedula, P. A deep learning enabler for non-intrusive reduced order modeling of fluid flows, Physics of Fluids, 31, 085101, 2019.

    Project 8: Digital Twin Laboratory

    Project Description: Digital twin can be 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. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In the projects related to DT lab, the students are expected to work towards developing a digital twin of a room. The digital twin should be indistinguishable from its physical counterpart. The projects will be broadly of two kinds: (1) experimental and hardware related and (2) algorithm development and software related for bringing physical realism in the DT.

    Some tentative topics which can be adapted to the interest of the students.

    1. Instrumentation of a room with diverse sensors to create a digital twin: This will involve installing microphones (for audio signals), RGB and hyperspectral camera, sterio-vision camera, thermal camera and drones equipped with air quality sensors.
    2. Visualization (AR and VR) tools for digital twin applications: This will involve developing visualization techniques and interface for interactions with the room via the DT.
    3. Enabling technologies of Digital Twin: Development of novel algorithms for real-time and accurate modeling of the relevant physics like Hybrid Analysis and Modeling, Compressed Sensing, Multivaratie Analysis Tools, Machine Learning
    4. Data assimilation, predictive modeling and uncertainty quantification for digital twins

    Supervisor: Adil Rasheed, NTNU
    Co-supervisors: Damiano Varagnolo, Annette Stahl NTNU Learning outcome: Digital Twin technologies, Machine Learning

    Reference:

    1. Rasheed, Adil; San, Omer; Kvamsdal, Trond. (2020) Digital Twin: Values, Challenges and Enablers from a modelling perspective. IEEE Access. vol. 8.

    Project 9: 3D Machine Learning

    Project Description: The project will focus on combining the two distinct fields of mesha generation and machine learning. The project will be conduected in collaboration with the Mathematics and Cybernetics Department of SINTEF Digital.

    The pre-project will involve the following tasks:

    1. Extensive literature survey on the topic of 3D Machine Learning
    2. Develop a good understanding of point cloud generation and mesh generation along with data strucutre representing thsoe meshes
    3. Collection and pre-processig of CAD models, point clouds and meshes from multiple sources
    4. Apply Machine Learning methods for the manipulation of the meshes and point clouds

    The pre-project will lead to a Masters project which can have the following applications

    1. Generative designs
    2. Deafeaturing
    3. Geometric shape optimization

    Supervisor: Adil Rasheed, NTNU
    Co-supervisor: Kjetil Andre Johannessen and George Muntingh, SINTEF Digital
    Learning outcome: 3D Machine Learning, CAD modeling, Mesh Generation

    Reference

    1. https://towardsdatascience.com/3d-deep-learning-made-easier-a-brief-introduction-to-facebooks-pytorch3d-framework-9fe3075f388a
    2. https://github.com/timzhang642/3D-Machine-Learning#pose_estimation

    Project 10: Dynamic Mode Decomposition informed deep neural network for object detection and classification (Affiliated to the Explainable AI project EXAIGON)

    Project description: Dynamic Mode Decomposition method originated in the fluid dynamics community tas a method to decompose complex flow fiels into a simple representation based on spatiotemporal coherent structures. More recently Grosek and Kutz, provides a novel application of the DMD atechnique and its dynamicsl decomsposition for state-of-teh art video processing. DMD modes with Fourier frequencies near the origin (zero frequencies) are interpreted as bacl-ground portions of the given video frames, and the modes with Foureir frequencies bounded away from the origin constitute their sparse counterparts. An approximate low-rank / sparse separation is achieved at the computational cost of just one SVD and one linear equatin solve.The DMD method can operate in real time with personal lamtop-class computing power and without any parameter tuning. This project will involve combining the strenghts of DMD to identify zones in a video stream where objects are moving with the power of convolutional neural network to only classify the image.

    The project will involve the following tasks:

    1. Extensive literature survey of multivariate methods with stress on DMD
    2. Reading of the work on Deep Neural Network based object detectiona and classification methods
    3. Combining DMD and DNN
    4. Writing down a report and / or article based on this work

    Supervisor: Adil Rasheed, NTNU
    Co-supervisor: Anastasios Lekkes, NTNU

    Reference

    1. Kutz N, Brunton SL, Brunton BW, Proctor JL, Dynamic Mode Decomposition, Data Driven Modeling of Complex Systems, SIAM

    Project 11:Data-driven physics discovery
    Proposals from Harald Martens, Idletechs AS, sorted by priority. Idletechs may ask for a standard report confidentiality period.

    1. From thermal video to thermal models. Equations of thermal dynamics from thermal video (Torbjørn Pedersen/Harald Martens, Idletechs AS). Main supervision: Adil Rasheed ITK). Thermal image segmentation, local OTFP modelling of individual image segments and ODE/PDE development from local OTFP scores.
    2. From 2D surface temperature video to model of 3D internal temperature dynamics. 3D metamodelling of thermal spatiotemporal dynamics: Simulations of Alcoa Anode temperature propagation, metamodelling, forecast of 3D temp. distribution from 2D thermal video. (Håkon Jarle Hassel/Harald Martens, Idletechs AS). Main supervision: Adil Rasheed ITK)
    3. Making better use of industrial vibration sensors. Multivariate vibration spectrogram modelling: 1D and/or 2D IDLE modelling of spectrograms from industrial vibration time series, based on physics-based and data-based hybrid modelling (log frequency cepstrum analysis, 1D and/or 2D motion estimation in continuous spectrograms. (Torbjørn Pedersen/Håkon Jarle Hassel/Harald Harald Martens, Idletechs AS/ Frank Westad, CAMO/NTNU/ Torbjørn Svensen NTNU). Main supervision: Adil Rasheed ITK)
    4. Faster and better estimation of non-rigid motions in industrial video monitoring. Multi-frame optical flow and intensity chang estimation in thermal video, RGB video and HSI video based on IDLE modelling (Harald Martens Idletechs AS/ Annette Stahl, ITK. Main supervision: Adil Rasheed ITK)

    Project 12:COLREG-compliant path following and collision avoidance with moving obstacles in the Trondheim fjord (Affiliated to the Explainable AI project AUTOSIT)
    Project description: This project concerns the problem of anticipating the intentions of another ship on time intervals in the order of minutes. Over such time intervals, one can typically expect the ship to perform certain manoeuvres (e.g., go straight, approach its destination port, make a complete evasive manoeuvre), but all such predictions will be highly uncertain. The goal of this project is to develop methods that are able to combine relevant sources of information to make predictions over such time intervals. The project will build upon the work by Meyer et al and will be realized through the following potential tasks.

    1. Data collection and preprocessing: This will involve collecting data from different sources as well as generating data (CAD models, weather data) if required and then pre-processing them for further analysis.
    2. Extending our reinforcement learning framework incorporating multiagent simulation capabilities
    3. More extensive incorporation of AIS data in the reinforcement learning framework
    4. Extension of the collision avoidance regulations in the current framework
    5. Devising better RL training strategies
    6. Run an ensemble of simulations using the trained agents to quantify uncertainty associated with long-term vessel predictions

    Supervisor: Adil Rasheed (adil.rasheed@ntnu.no)
    Co-supervisor: Edmund Brekke (Edmund.brekke@ntnu.no), Morten Breivik (morten.breivik@ntnu.no)


    References
    1. Meyer E., Robinson, H., Rasheed, A., and San, O., Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning, IEEE Access, 8, 41466-41481, 2020
    2. Meyer E, Heibergh A, Rasheed A and San O, COREG-compliant path following and collision avoidance with moving obstacles in the Trondheim fjord using Deep Reinforcement Learning, In preparation
    3. Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles

    Project 13:Creating a bussiness model in the cloud using Digital Twin
    Contact me to discuss the idea and how to proceed in this intersting direction

    Supervisor: Adil Rasheed (adil.rasheed@ntnu.no)
    Co-supervisor: Karl Johan Haarberg (karl.johan.haarberg@bipartner.no) and Gunnar Haugen (gunnar.haugen@bipartner.no)



    Project 14: Digital Twins in precision production of healthcare services: Using case clinical pathways for colon and rectal cancer

    Background: This project is a continuation of a project and consequent master thesis performed in 2019 / 2020 comprising a collaboration between Prediktor and the Østfold Hospital Trust (ØHT). The project will consider the following setup: as a patient moves through her/his medical pathway (i.e., departments, visits, hospitals, etc.) he/she will produce a series of data relative to her/his medical/clinical, logistics and economical aspects. Importantly, this data can collected from different sensors/instruments into centralized databases. This information can then be used to both model important medical aspects of that single patient, but also have a better overview of the medical healthcare system as a whole.
    Aim and objectives: If enough data is available, it is meaningful to use Machine Learning concepts for creating understanding that helps both curing that patient, improving the treatments for others, and optimizing the healthcare services in a holistic way. The aim of the project is thus to focus on porting modelling and data analysis technologies used typically in the process industry to the case of healthcare. Importantly, the thesis starts from the successful proof of concepts projects that have been developed by Østfold Hospital Trust together with Prediktor and Idletechs. The objectives are thus to develop healthcare digital twin models startin from datasets from real biomedical applications.
    Supervisor: Øivind Riis (oivind.riis@ntnu.no)
    Co-supervisor: Adil Rasheed (adil.rasheed@ntnu.no) and Damiano Varagonolo (damiano.varagnolo@ntnu.no)


PhD students

Haakon Robinson
Department of Engineering Cybernetics, NTNU
Topic: Hybrid Modeling
Domain: Bigdata Cybernetics
Homepage: Click here

Erlend Lundby
Department of Engineering Cybernetics, NTNU
Topic: Big Data in the process industry
Domain: Bigdata Cybernetics
Homepage: Click here

Abdallah Alshantti
Department of Engineering Cybernetics, NTNU
Topic: Finance and Banking
Domain: Data-driven modeling
Homepage: Click here

Prateek Gupta
Department of Marine Technology, NTNU
Topic: Ship performance monitoring and optimization using in-service measurements and big data analysis methods
Domain: Marine Hydrodynamics
Homepage: Click here
Status: Ongoing

Aoudou Midjiyawa Zakari
Department of Mathematical Sciences, NTNU
Topic: Wind modeling in fjord
Domain: Atmospheric Modeling
Homepage: Click here
Status: Ongoing

Muhammad Salman Siddiqui
Department of Mathematical Sciences, NTNU
Topic: Reduced Order Modeling of Wind Turbine
Domain: Wind Energy
Homepage: Click here
Status: Complete