  A recent study in turbulent flow simulation demonstrated the potential of
generative diffusion models for fast 3D surrogate modeling. This approach
eliminates the need for specifying initial states or performing lengthy
simulations, significantly accelerating the process. While adept at sampling
individual frames from the learned manifold of turbulent flow states, the
previous model lacks the capability to generate sequences, hindering analysis
of dynamic phenomena. This work addresses this limitation by introducing a 4D
generative diffusion model and a physics-informed guidance technique that
enables the generation of realistic sequences of flow states. Our findings
indicate that the proposed method can successfully sample entire subsequences
from the turbulent manifold, even though generalizing from individual frames to
sequences remains a challenging task. This advancement opens doors for the
application of generative modeling in analyzing the temporal evolution of
turbulent flows, providing valuable insights into their complex dynamics.
