Browsing NTNU Open by Author "San, Omer"
Now showing items 1-20 of 41
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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 ... -
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 ... -
Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality
Elfarri, Elias Mohammed; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2023)A digital twin is a powerful tool that can help monitor and optimize physical assets in real-time. Simply put, it is a virtual representation of a physical asset, enabled through data and simulators, that can be used for ... -
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 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. ... -
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 ... -
Digital Twin: Values, Challenges and Enablers from a modelling perspective
Rasheed, Adil; San, Omer; Kvamsdal, Trond (Journal article; Peer reviewed, 2020)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 ... -
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
Stadtmann, Florian; Rasheed, Adil; Kvamsdal, Trond; Johannessen, Kjetil Andre; San, Omer; Kölle, Konstanze; Tande, John Olav Giæver; Barstad, Idar; Benhamou, Alexis; Brathaug, Thomas; Christiansen, Tore; Firle, Anouk-Letizia; Fjeldly, Alexander; Frøyd, Lars; Gleim, Alexander; Høiberget, Alexander; Meissner, Catherine; Nygård, Guttorm; Olsen, Jørgen; Paulshus, Håvard; Rasmussen, Tore; Rishoff, Elling; Scibilia, Francesco; Skogås, John Olav (Peer reviewed; Journal article, 2023) -
Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions
Sørbø, Sondre; Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2024)With the recent wave of digitalization, specifically in the context of safety–critical applications, there has been a growing need for computationally efficient, accurate, generalizable, and trustworthy models. Physics-based ... -
An evolve-then-correct reduced order model for hidden fluid dynamics
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Journal article; Peer reviewed, 2020)n this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. Specifically, we split the underlying dynamics ... -
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data
Vaddireddy, Harsha; Rasheed, Adil; Staples, Anne; San, Omer (Peer reviewed; Journal article, 2020)We put forth a modular approach for distilling hidden flow physics from discrete and sparse observations. To address functional expressiblity, a key limitation of the black-box machine learning methods, we have exploited ... -
Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction
Ahmed, Shady E; Bhar, Kinjal; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends ... -
Frame invariant neural network closures for Kraichnan turbulence
Pawar, Suraj; San, Omer; Rasheed, Adil; Vedula, Prakash (Peer reviewed; Journal article, 2022)Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization ... -
GANs enabled super-resolution reconstruction of wind field
Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Tabib, Mandar; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ... -
GANs enabled super-resolution reconstruction of wind field
Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ... -
Geometric Change Detection in Digital Twins using 3D Machine Learning
Sundby, Tiril; Graham, Julia Maria; Rasheed, Adil; Tabib, Mandar; San, Omer (Peer reviewed; Journal article, 2021)Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations ... -
Hybrid analysis and modeling for next generation of digital twins
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2021)The physics-based modeling has been the workhorse for many decades in many scientific and engineering applications ranging from wind power, weather forecasting, and aircraft design. Recently, data-driven models are ...