Blar i NTNU Open på forfatter "Rasheed, Adil"
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A deep learning enabler for non-intrusive reduced order modeling of fluid flows
Pawar, Suraj; Rahman, Sk. Mashfiqur; Vaddireddy, H; San, Omer; Rasheed, Adil; Vedula, Prakash (Journal article; Peer reviewed, 2019)In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict ... -
A full-scale 3D Vs 2.5D Vs 2D analysis of flow pattern and forces for an industrial-scale 5MW NREL reference wind-turbine
Tabib, Mandar; Rasheed, Adil; Siddiqui, Muhammad Salman; Kvamsdal, Trond (Journal article; Peer reviewed, 2017)NREL 5MW reference turbine, which is a popular, realistic and standardized industrial scale offshore turbine model, is used in this work for understanding the associated flow-complexities and for testing models. For such ... -
A proof-of-concept Digital Twin implementation for monitoring patients through the Clinical Pathway for Prostate Cancer in the Norwegian Health and Care Service
Pedersen, Olav Landmark (Master thesis, 2021) -
A Step Towards a Reduced Order Modelling of Flow Characterized by Wakes Using Proper Orthogonal Decomposition
Fonn, Eivind; Rasheed, Adil; Tabib, Mandar; Kvamsdal, Trond (Chapter; Peer reviewed, 2017)High fidelity simulations of flow might be quite demanding, because they involve up to O(106 – 109) degrees of freedom and several hours (or even days) of computational time, also on powerful hardware parallel architectures. ... -
A step towards reduced order modelling of flow characterized by wakes using Proper Orthogonal Decomposition
Fonn, Eivind; Tabib, Mandar; Siddiqui, Muhammad Salman; Rasheed, Adil; Kvamsdal, Trond (Journal article; Peer reviewed, 2017)High fidelity simulations of flow can be quite demanding, involving up to O(106) to O(109) degrees of freedom, and several hours or days of computational time, even on powerful parllel architectures. These techniques become ... -
A strategy controller for concave obstacle avoidance
Nakken, Daniel (Master thesis, 2021)Denne masteroppgaven utforsker potensialet av forsterkende læring (engelsk: reinforcement learning) for applikasjoner av robotisk manipulator kollisjons unngåelse. Oppgaven presenterer en Proximal Policy Optimization (PPO) ... -
Applications of Data-Driven Equation Discovery to Synthetic and Experimental Data
Sandvik, Fredrik Pettersen (Master thesis, 2021)Fysikkbasert modellering kan være svært nøyaktig og oversiktlig, men krever omfattende kunnskap om dynamikken i systemet man ønsker å modellere. Datadrevne metoder kan i stedet basere seg på observasjonsdata og ikke ... -
Applying Object Detection to Marine Data and Exploring Explainability of a Fully Convolutional Neural Network Using Principal Component Analysis
Stavelin, Peter Herman; Rasheed, Adil; San, Omer; Hestnes, Arne Johan (Peer reviewed; Journal article, 2021)With the rise of focus on man made changes to our planet and wildlife therein, more and more emphasis is put on sustainable and responsible gathering of resources. In an effort to preserve maritime wildlife the Norwegian ... -
Big Data Analytics As a Tool to Monitor Hydrodynamic Performance of a Ship
Gupta, Prateek; Steen, Sverre; Rasheed, Adil (Chapter, 2019)A modern ship is fitted with numerous sensors and Data Acquisition Systems (DAQs) each of which can be viewed as a data collection source node. These source nodes transfer data to one another and to one or many centralized ... -
Building a digital twin of the thermodynamic behaviour of a building using hybrid modeling
Myklebust, Annfrid Hopland (Master thesis, 2022)Denne oppgaven presenterer et Hybrid Analysis and Modeling(HAM) rammeverk med det formål å realisere en prediktiv digital tvilling (DT) av den termodynamiske oppførselen til en boligbygning. En DT er en digital ... -
COLREG-Compliance for Autonomous Surface Vehicles using Deep Reinforcement Learning
Heiberg, Amalie (Master thesis, 2020)Bruken av og forskning innen autonome systemer har økt kraftig i senere år, inkludert i marin sektor. Ettersom transportsektoren samtidig gjennomgår en omfattende elektrifisering, lover autonom skipsfart ikke bare reduserte ... -
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Meyer, Eivind; Heiberg, Amalie; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading ... -
Combining Grid-Based Uncertainty Propagation and Neural Networks With Uncertainty Estimation
Vesterkjær, Eirik Ekjord (Master thesis, 2020)Punktbaserte metoder for deterministisk videresending av usikkerhet kan benyttes i kombinasjon med usikkerhetsestimering for nevrale nettverk. Dette kan brukes for å modellere dynamiske tilstandsromprosesser samt deres ... -
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety–critical applications require accurate, interpretable, computationally efficient, and generalizable models. ... -
Comparing Deep Reinforcement Learning Algorithms’ Ability to Safely Navigate Challenging Waters
Larsen, Thomas Nakken; Teigen, Halvor Ødegård; Laache, Torkel; Varagnolo, Damiano; Rasheed, Adil (Peer reviewed; Journal article, 2021)Reinforcement Learning (RL) controllers have proved to effectively tackle the dual objectives of path following and collision avoidance. However, finding which RL algorithm setup optimally trades off these two tasks is not ... -
Convolutional Neural Network and Generative Adversarial Networks Enabled Resolution Enhancement of Numerical Simulations
Tran, Duy Tan Huynh (Master thesis, 2020)Optimal vindmølleplassering og prognoser av vindmøllers kraftproduksjon krever nøyaktig kunnskap om vindfeltet. Generelt blir målekampanjer foretatt for å innhente informasjon om de rådende vindforholdene i et bestemt ... -
Data-driven recovery of hidden hysics in reduced order modeling of fluid flows
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework ... -
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework ... -
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA)—a novel approach to Hybrid Analysis and Modeling (HAM). The objective of HAM is to combine physics-based modeling (PBM) and ... -
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Havenstrøm, Simen Theie; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an ...