no code implementations • 31 Mar 2024 • Thomas Nakken Larsen, Eirik Runde Barlaug, Adil Rasheed
Modern control systems are increasingly turning to machine learning algorithms to augment their performance and adaptability.
no code implementations • 31 Mar 2024 • Abdallah Alshantti, Adil Rasheed, Frank Westad
In doing so we also consider the situation for which re-identification attacks are formulated as reconstruction attacks, i. e., the situation where an attacker uses evolutionary multi-objective optimisation for perturbing synthetic samples closer to the training space.
no code implementations • 27 Mar 2024 • Daniel Menges, Trym Tengesdal, Adil Rasheed
This article proposes an approach for collision avoidance, path following, and anti-grounding of autonomous surface vessels under consideration of environmental forces based on Nonlinear Model Predictive Control (NMPC).
no code implementations • 27 Mar 2024 • Daniel Menges, Adil Rasheed
While RPCA offers an enhanced alternative to traditional Principal Component Analysis (PCA) for high-dimensional data management, the scope of this work extends its utilization, focusing on robust, data-driven modeling applicable to huge data sets in real-time.
no code implementations • 4 Jan 2024 • Florian Stadtmann, Hary Pirajan Mahalingam, Adil Rasheed
In this work, a data integration framework for static and real-time data from various sources on the assets and their environment is presented that allows collecting and processing of data in Python and deploying the data in real-time through Unity on different devices, including virtual reality headsets.
no code implementations • 4 Dec 2023 • Aksel Vaaler, Svein Jostein Husa, Daniel Menges, Thomas Nakken Larsen, Adil Rasheed
Results demonstrate the PSF's effectiveness in maintaining safety without hindering the RL agent's learning rate and performance, evaluated against a standard RL agent without PSF.
1 code implementation • 18 Sep 2023 • Jacob Wulff Wold, Florian Stadtmann, Adil Rasheed, Mandar Tabib, Omer San, Jan-Tore Horn
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 intractable.
no code implementations • 5 Jul 2023 • Florian Stadtmann, Adil Rasheed, Tore Rasmussen
In this work, a digital twin with standalone, descriptive, and predictive capabilities is created for an existing onshore wind farm located in complex terrain.
1 code implementation • 1 Jul 2023 • Abdallah Alshantti, Damiano Varagnolo, Adil Rasheed, Aria Rahmati, Frank Westad
In this work, we design a cascaded tabular GAN framework (CasTGAN) for generating realistic tabular data with a specific focus on the validity of the output.
no code implementations • 16 Apr 2023 • Florian Stadtman, Adil Rasheed, Trond Kvamsdal, Kjetil André Johannessen, Omer San, Konstanze Kölle, John Olav Giæver Tande, Idar Barstad, Alexis Benhamou, Thomas Brathaug, Tore Christiansen, Anouk-Letizia Firle, Alexander Fjeldly, Lars Frøyd, Alexander Gleim, Alexander Høiberget, Catherine Meissner, Guttorm Nygård, Jørgen Olsen, Håvard Paulshus, Tore Rasmussen, Elling Rishoff, Francesco Scibilia, John Olav Skogås
The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.
no code implementations • 3 Apr 2023 • Florian Stadtmann, Henrik Gusdal Wassertheurer, Adil Rasheed
Furthermore, we demonstrate a standalone digital twin, a descriptive digital twin, and a prescriptive digital twin of an operational floating offshore wind turbine.
no code implementations • 24 Feb 2023 • Erlend Torje Berg Lundby, Adil Rasheed, Ivar Johan Halvorsen, Dirk Reinhardt, Sebastien Gros, Jan Tommy Gravdahl
This simulated dataset can be used in a static deep active learning acquisition scheme referred to as global explorations.
no code implementations • 2 Jan 2023 • Erlend Torje Berg Lundby, Haakon Robinsson, Adil Rasheed, Ivar Johan Halvorsen, Jan Tommy Gravdahl
Neural networks are rapidly gaining interest in nonlinear system identification due to the model's ability to capture complex input-output relations directly from data.
no code implementations • 14 Dec 2022 • Elias Mohammed Elfarri, Adil Rasheed, Omer San
By understanding the capability level of a digital twin, we can better understand its potential and limitations.
no code implementations • 15 Nov 2022 • Daniel Menges, Adil Rasheed
To investigate the capability of this observer framework, the environmental disturbances are simulated dynamically under consideration of different model and measurement uncertainties.
no code implementations • 22 Sep 2022 • Haakon Robinson, Erlend Lundby, Adil Rasheed, Jan Tommy Gravdahl
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control.
no code implementations • 13 Sep 2022 • Erlend Torje Berg Lundby, Adil Rasheed, Ivar Johan Halvorsen, Jan Tommy Gravdahl
In this work, we demonstrate the value of sparse regularization techniques to significantly reduce the model complexity.
no code implementations • 15 Aug 2022 • Omer San, Suraj Pawar, Adil Rasheed
Physics-based models have been mainstream in fluid dynamics for developing predictive models.
no code implementations • 7 Jul 2022 • Omer San, Suraj Pawar, Adil Rasheed
In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications.
no code implementations • 7 Jul 2022 • Omer San, Suraj Pawar, Adil Rasheed
A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions.
no code implementations • 7 Jun 2022 • Sindre Stenen Blakseth, Adil Rasheed, Trond Kvamsdal, Omer San
In the current work, we demonstrate how a hybrid approach combining the best of PBM and DDM can result in models which can outperform them both.
no code implementations • 25 May 2022 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu, Alessandro Veneziani
We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost.
no code implementations • 13 May 2022 • Haakon Robinson, Suraj Pawar, Adil Rasheed, Omer San
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention.
1 code implementation • 15 Oct 2021 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu
Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space.
no code implementations • 7 Oct 2021 • Prateek Gupta, Adil Rasheed, Sverre Steen
The current work uses machine-learning (ML) methods to estimate the hydrodynamic performance of a ship using the onboard recorded in-service data.
no code implementations • 24 May 2021 • Sindre Stenen Blakseth, Adil Rasheed, Trond Kvamsdal, Omer San
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) -- a novel approach to Hybrid Analysis and Modeling (HAM).
no code implementations • 26 Mar 2021 • Omer San, Adil Rasheed, Trond Kvamsdal
Most modeling approaches lie in either of the two categories: physics-based or data-driven.
no code implementations • 15 Mar 2021 • Tiril Sundby, Julia Maria Graham, Adil Rasheed, Mandar Tabib, Omer San
Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica.
no code implementations • 22 Dec 2020 • Andrine Elsetrønning, Adil Rasheed, Jon Bekker, Omer San
A vital part of using the lung sound for disease detection is discrimination between normal lung sound and abnormal lung sound.
Sound Audio and Speech Processing
1 code implementation • 18 Dec 2020 • Suraj Pawar, Omer San, Burak Aksoylu, Adil Rasheed, Trond Kvamsdal
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences.
no code implementations • 5 Aug 2020 • Shady Ahmed, Suraj Pawar, Omer San, Adil Rasheed, Mandar Tabib
We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements.
1 code implementation • 17 Jun 2020 • Shady E. Ahmed, Omer San, Kursat Kara, Rami Younis, Adil Rasheed
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws.
no code implementations • 17 Jun 2020 • Simen Theie Havenstrøm, Adil Rasheed, Omer San
Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems.
no code implementations • 16 Jun 2020 • Eivind Meyer, Amalie Heiberg, Adil Rasheed, Omer San
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics.
no code implementations • 28 May 2020 • Shady Ahmed, Suraj Pawar, Omer San, Adil Rasheed
In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements.
Dynamical Systems Computational Physics Fluid Dynamics
1 code implementation • 21 May 2020 • Shady E. Ahmed, Kinjal Bhar, Omer San, Adil Rasheed
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.
Dynamical Systems Fluid Dynamics
no code implementations • 11 Feb 2020 • Herman Stavelin, Adil Rasheed, Omer San, Arne Johan Hestnes
In an effort to preserve maritime wildlife the Norwegian government has decided that it is necessary to create an overview over the presence and abundance of various species of wildlife in the Norwegian fjords and oceans.
no code implementations • 18 Dec 2019 • Eivind Meyer, Haakon Robinson, Adil Rasheed, Omer San
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 underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way.
1 code implementation • 14 Dec 2019 • Shady E. Ahmed, Omer San, Adil Rasheed, Traian Iliescu
In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes.
Fluid Dynamics Dynamical Systems Computational Physics
no code implementations • 9 Oct 2019 • Haakon Robinson, Adil Rasheed, Omer San
It has been shown that neural networks with piecewise affine activation functions are themselves piecewise affine, with their domains consisting of a vast number of linear regions.