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physics.flu-dyn2026
Conditional diffusion denoising probabilistic model for super-resolution of atmospheric boundary layer large eddy simulation
Omar Sallam, Mirjam Fürth
Climate change necessitates rapid expansion of renewable energy, with wind energy offering a scalable and low-impact solution. However, accurate prediction of wind loads and power…
physics.flu-dyn2024
Inference of water waves surface elevation from horizontal velocity components using physics informed neural networks (PINN)
Omar Sallam, Mirjam Fürth
In this paper, a mathematical model is presented to infer the wave free surface elevation from the horizontal velocity components using Physics Informed Neural Network (PINN). PINN…
physics.flu-dyn2024
Dual scale Residual-Network for turbulent flow sub grid scale resolving: A prior analysis
Omar Sallam, Mirjam Fürth
This paper introduces generative Residual Networks (ResNet) as a surrogate Machine Learning (ML) tool for Large Eddy Simulation (LES) Sub Grid Scale (SGS) resolving. The study inve…