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English(EN) ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows

新管道为3D流体动力学模拟生成机器学习就绪数据集

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ChannelFlow-Tools，一个开源管道，旨在为3D阻塞通道流生成机器学习就绪数据集。这个驱动式配置系统集成了程序化障碍物生成、符号距离场体素化和Lattice-Boltzmann模拟，以创建计算流体动力学数据集。该管道确保几何生成的字节一致可重复性，并经过广泛验证，证明了其能够为训练代理模型生成物理上一致的数据。 AI

影响 能够为CFD代理模型生成更健壮和可审计的训练数据，可能加速该领域的研究。

排序理由 研究论文，详细介绍了一个用于生成机器学习数据集的新工具。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新管道为3D流体动力学模拟生成机器学习就绪数据集

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研究论文，详细介绍了一个用于生成机器学习数据集的新工具。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shubham Kavane, Lukas Schr\"oder, Kajol Kulkarni, Fernando Gonzalez, Harald Koestler ·

    ChannelFlow-Tools：一种驱动式配置的管道，用于生成机器学习就绪的3D阻塞通道流数据集

    arXiv:2509.15236v2 Announce Type: replace-cross Abstract: Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the training data. These models are typically trained on fixed, pre-generated datasets. Sy…

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