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New AneumoBench dataset evaluates synthetic-geometry transfer for aneurysm CFD

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced AneumoBench, a new benchmark dataset designed to evaluate synthetic-geometry transfer for aneurysm computational fluid dynamics (CFD). The dataset links 401 source aneurysm geometries to over 9,000 locally edited descendants, each with computed CFD fields. This resource enables controlled comparisons of various training strategies and architectures, such as GraphSAGE, to assess their effectiveness in predicting unseen geometries and fluid dynamics, particularly for steady-field prediction and wall shear stress forecasting. AI

IMPACT Provides a standardized benchmark for evaluating synthetic-geometry transfer methods in scientific machine learning for CFD applications.

RANK_REASON The cluster contains a research paper detailing a new benchmark dataset and associated protocols for scientific machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AneumoBench dataset evaluates synthetic-geometry transfer for aneurysm CFD

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The cluster contains a research paper detailing a new benchmark dataset and associated protocols for scientific machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [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: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD

    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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