Conditional neural field latent diffusion model for generating spatiotemporal turbulence.

ABSTRACT: Eddy-resolving turbulence simulations are essential for understanding and controlling complex unsteady fluid dynamics, with significant implications for engineering and scientific applications. Traditional numerical methods, such as direct numerical simulations (DNS) and large eddy simulations (LES), provide high accuracy but face severe computational limitations, restricting their use in high-Reynolds number or real-time scenarios. Recent advances in deep learning-based surrogate models offer a promising alternative by providing efficient, data-driven approximations. However, these models often rely on deterministic frameworks, which struggle to capture the chaotic and stochastic nature of turbulence, especially under varying physical conditions and complex, irregular geometries. Here, we

SUBMITTER: Du P 

PROVIDER: S-EPMC11607081 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature