4 papers
Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
Phillip Si, Yuan Qiu, Omar Sallam +4
AI-driven flood digital twins demand fast hydrodynamic surrogates for ensemble forecasting and observation assimilation. Yet even GPU-accelerated two-dimensional shallow water equa…
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…
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…
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…