ArticlePhysical review fluids2025
CoNFiLD-inlet: Synthetic turbulence inflow using generative latent diffusion models with neural fields.
Article in Physical review fluids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching.Journal of computational physics · 2026Article
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- CoNFiLD-inlet: Synthetic turbulence inflow using generative latent diffusion models with neural fields.Physical review fluids · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multiscale structures of turbulence. Traditional recycling-based methods rely on computationally expensive precursor simulations while existing synthetic inflow generators often fail to reproduce realistic coherent structures of turbulence. Recent advances in deep learning (DL) have opened new possibilities for inflow turbulence generation, yet many DL-based methods rely on deterministic, autoregressive frameworks prone to error accumulation, resulting in poor robustness for long-term predictions. In this work, we present CoNFiLD-inlet, a novel DL-based inflow turbulence generator that integrates diffusion models with a conditional neural field to produce realistic, stochastic inflow turbulence. By parametrizing inflow conditions using Reynolds numbers, CoNFiLD-inlet generalizes effectively across a wide range of Reynolds numbers (
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