Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation

Understand

Turbulent flows have historically presented formidable challenges to predictive computational modeling.

• Traditional numerical simulations often require vast computational resources, making them infeasible for numerous engineering applications.
• As an alternative, deep learning-based surrogate models have emerged, offering data-drive solutions.
• However, these are typically constructed within deterministic settings, leading to shortfall in capturing the innate chaotic and stochastic behaviors of turbulent dynamics.