Turbulence Simulation

Turbulence simulation aims to accurately model the complex, chaotic motion of fluids, a computationally expensive task crucial for numerous applications. Current research focuses on developing efficient surrogate models, employing techniques like Fourier neural operators, denoising diffusion probabilistic models, and physics-informed neural networks, to reduce computational cost and improve accuracy, often incorporating multi-fidelity approaches to leverage both high- and low-fidelity data. These advancements are improving the prediction of turbulent flows in diverse fields, from climate modeling and aerospace engineering to additive manufacturing and turbulence mitigation in imaging. The ultimate goal is to create robust, accurate, and computationally feasible simulations for a wider range of applications.