Browsing SINTEF Open by Author "Pawar, Suraj"
Now showing items 1-5 of 5
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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 ... -
Multi-fidelity information fusion with concatenated neural networks
Pawar, Suraj; San, Omer; Vedula, Prakash; Rasheed, Adil; Kvamsdal, Trond (Peer reviewed; Journal article, 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 non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold
Tabib, Mandar; Pawar, Suraj; Ahmed, Shady E; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2021)In this study, we present a parametric non-intrusive reduced order modeling framework as a potential digital twin enabler for fluid flow related applications. The case study considered here involves building-induced flows ... -
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 ... -
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 ...