FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation
Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accumulate rapidly over rollout. In this paper, we propose FlowRefiner, a flow matching-based iterative refinement framework for 3D turbulent flow simulation. The method replaces stochastic denoising refinement with deterministic ODE-based correction, uses a unified velocity-field regression objective across all refinement stages, and introduces a decoupled sigma schedule that fixes the noise range independently of refinement depth. These design choices yield stable and effective refinement in the small-noise regime. Experiments on large-scale 3...
| Authors | Yilong Dai, Yiming Sun, Yiheng Chen et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 18 April 2026 |
| Last updated at source | 25 April 2026 |
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