21 papers
A hybrid proper orthogonal decomposition and diffusion framework for reduced-order forecasting of turbulent flow dynamics
Rodrigo Abadia-Heredia, Xiangrui Zou, Manuel Lopez-Martin +2
Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotempo…
Data-driven impeller model for efficient large eddy simulations of metastable von Kármán flows
Quentin Malé, Quentin Malé, Lucas Amoudruz +6
The von Kármán turbulent swirling flow exhibits intriguing large-scale metastable dynamics, including low-frequency state switching. The study of state switching demands long-durat…
A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou +2
Forward propagation of input uncertainties in physics-based wildfire models is computationally prohibitive, limiting the use of high-fidelity simulators in risk assessment workflow…
Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
Lucas Amoudruz, Sergey Litvinov, Costas Papadimitriou +1
Inverse problems are crucial for many applications in science, engineering and medicine that involve data assimilation, design, and imaging. Their solution infers the parameters or…
Structure tensor Reynolds-averaged Navier-Stokes turbulence models with equivariant neural networks
Aaron Miller, Sahil Kommalapati, Robert Moser +1
Accurate and generalizable Reynolds-averaged Navier-Stokes (RANS) models for turbulent flows rely on effective closures, but currently available closures are notoriously unreliable…
Prediction of Extreme Events in Multiscale Simulations of Geophysical Turbulence using Reinforcement Learning
Yifei Guan, Lucas Amoudruz, Sergey Litvinov +4
Accurate subgrid-scale closures are essential for weather/climate models, where predicting extreme events is critical. Traditional closures have structural errors, e.g., producing…