Blar i NTNU Open på forfatter "San, Omer"
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Hybrid deep-learning POD-based parametric reduced order model for flow around wind-turbine blade
Tabib, Mandar; Tsiolakis, Vasileios; Pawar, Suraj; Ahmed, Shady E.; Rasheed, Adil; Kvamsdal, Trond; San, Omer (Peer reviewed; Journal article, 2022)In this study, we present a parametric, non-intrusive reduced order modeling (NIROM) framework as a potential digital-twin enabler for fluid flow around an aerofoil. A wind turbine blade has its basic foundation in the ... -
Interface learning of multiphysics and multiscale systems
Ahmed, Shady E; San, Omer; Kara, Kursat; Younis, Rami; Rasheed, Adil (Peer reviewed; Journal article, 2020)Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm ... -
A long short term memory for hybrid uplifted reduced order models.
Ahmed, Shady E; San, Omer; Rasheed, Adil; Trian, Iliescu (Peer reviewed; Journal article, 2020)In this paper, we introduce an uplifted reduced order modeling (UROM) approach through the integration of standard projection based methods with long short-term memory (LSTM) embedding. Our approach has three modeling ... -
Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil; Navon, Ionel M (Peer reviewed; Journal article, 2020)Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor–corrector ... -
Memory embedded non-intrusive reduced order modeling of non-ergodic flows
Ahmed, Shady E; Rahman, Sk. Mashfiqur; San, Omer; Rasheed, Adil; Navon, Ionel M (Journal article; Peer reviewed, 2019)Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, ... -
Model fusion with physics-guided machine learning: Projection-based reduced-order modeling
Pawar, Suraj; San, Omer; Nair, Aditya; Rasheed, Adil; Kvamsdal, Trond (Journal article; Peer reviewed, 2021)The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially ... -
Multi-fidelity information fusion with concatenated neural networks
Pawar, Suraj; San, Omer; Vedula, Prakash; Rasheed, Adil; Kvamsdal, Trond (Journal article; Peer reviewed, 2022)Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design ... -
Multifidelity computing for coupling full and reduced order models
Ahmed, Shady E; San, Omer; Kara, Kursat; Younis, Rami; Rasheed, Adil (Peer reviewed; Journal article, 2021)Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal ... -
A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations
Pawar, Suraj; San, Omer; Rasheed, Adil; Navon, Ionel M. (Peer reviewed; Journal article, 2021)This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitable for situations where the mechanistic ... -
Nonlinear proper orthogonal decomposition for convection-dominated flows
Ahmed, Shady E; San, Omer; Rasheed, Adil; Trian, Iliescu (Peer reviewed; Journal article, 2021)Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical ... -
A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil; Tabib, Mandar (Journal article, 2020)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. Toward emerging applications of ... -
On closures for reduced order models— A spectrum of first-principle to machine-learned avenues
Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil; Trian, Iliescu; Noack, Bernd R. (Peer reviewed; Journal article, 2021)For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr–Sommerfeld stability equation and numerous vortex ... -
Physics guided machine learning using simplified theories
Pawar, Suraj; San, Omer; Aksoylu, Burak; Rasheed, Adil; Kvamsdal, Trond (Peer reviewed; Journal article, 2021)Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular ... -
Physics guided neural networks for modelling of non-linear dynamics
Robinson, Haakon; Pawar, Suraj; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2022)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 ... -
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
Pawar, Suraj; San, Omer; Rasheed, Adil; Vedula, Prakash (Peer reviewed; Journal article, 2020)In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser ... -
Prospects of federated machine learning in fluid dynamics
San, Omer; Pawar, Suraj; Rasheed, Adil (Peer reviewed; Journal article, 2022)Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, ... -
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)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. We build on the fact that in a realistic ... -
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning
Heiberg, Amalie; Larsen, Thomas Nakken; Meyer, Eivind; Rasheed, Adil; San, Omer; Varagnolo, Damiano (Peer reviewed; Journal article, 2022)Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower ... -
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
Meyer, Eivind; Robinson, Haakon; 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 ... -
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
Meyer, Eivind; Robinson, Haakon; 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 ...