PulseAugur
EN
LIVE 09:23:23

New Dataset and Pipeline for AI Modeling of Turbulent Flows

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a validated dataset and pipeline for training neural operators to model turbulent 3D obstructed channel flows. The lattice Boltzmann solver used in the pipeline has been rigorously verified against experimental measurements, including Strouhal number and drag coefficients. This work aims to enable standardized comparison of surrogate models like Fourier Neural Operator and U-Net variants for tasks such as forecasting and super-resolution, using physics-informed metrics to assess their representation of turbulent energy cascades. AI

IMPACT Enables more rigorous evaluation and comparison of neural operators for complex fluid dynamics simulations.

RANK_REASON The cluster contains an academic paper detailing a new dataset and pipeline for AI modeling, fitting the 'research' bucket.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Lukas Schr\"oder, Shubham Kavane, Harald K\"ostler ·

    A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows

    arXiv:2606.16765v1 Announce Type: new Abstract: Evaluating neural operators for 3D turbulent flow requires validated datasets with physical benchmarks. We present a reproducible pipeline generating training data for 3D channel flows around generated geometries at Re=1,000-10,000.…

  2. arXiv cs.LG TIER_1 English(EN) · Harald Köstler ·

    A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows

    Evaluating neural operators for 3D turbulent flow requires validated datasets with physical benchmarks. We present a reproducible pipeline generating training data for 3D channel flows around generated geometries at Re=1,000-10,000. Our lattice Boltzmann solver with cumulant coll…

RELATED ENTITIES

RELATED TOPICS