Browsing NTNU Open by Author "Pawar, Suraj"
Now showing items 1-19 of 19
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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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
San, Omer; Pawar, Suraj; Rasheed, Adil (Peer reviewed; Journal article, 2022)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 ...