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English(EN) AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD

新的AneumoBench数据集评估动脉瘤CFD的合成几何迁移

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出AneumoBench，这是一个新的基准数据集，旨在评估动脉瘤计算流体动力学（CFD）的合成几何迁移。该数据集将401个源动脉瘤几何体与其9000多个局部编辑的后代关联起来，每个后代都计算了CFD场。该资源能够对GraphSAGE等各种训练策略和架构进行受控比较，以评估它们在预测未知几何体和流体动力学方面的有效性，特别是对于稳态场预测和壁面剪切应力预测。 AI

影响 为评估CFD应用科学机器学习中的合成几何迁移方法提供了一个标准化的基准。

排序理由 该集群包含一篇研究论文，详细介绍了新的基准数据集和相关的科学机器学习协议。[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 →

新的AneumoBench数据集评估动脉瘤CFD的合成几何迁移

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该集群包含一篇研究论文，详细介绍了新的基准数据集和相关的科学机器学习协议。[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) · Xigui Li, Yuanye Zhou, Feiyang Xiao, Xin Guo, Chen Jiang, Tan Pan, Xingmeng Zhang, Cenyu Liu, Zeyun Miao, Xiansheng Wang, Qimeng Wang, Yichi Zhang, Wenbo Zhang, Hongwei Zhang, Ruoxi Jiang, Fengping Zhu, Limei Han, Chensen Lin, Yuan Cheng ·

    AneumoBench：用于动脉瘤CFD中合成几何迁移的源链接基准测试

    arXiv:2505.14717v2 Announce Type: replace-cross Abstract: Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape editing can expand limited geometry collections, but whether its variants improve…

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