English(EN) Data-Free Finite-Volume Convolutional Neural Networks for Steady Compressible Euler Flow around a Circular Cylinder

新开发的无数据CNN用于流体动力学模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了一种新的无数据有限体积卷积神经网络（FV-CNN），旨在模拟圆形柱体周围的稳态可压缩欧拉流。该方法利用共享面通量进行保守空间平衡，并使用自动微分计算参数梯度，旨在通过解决导数不匹配问题来改进先前的AD-CNN方法。虽然FV-CNN在过渡宽度比和局部清晰度等某些指标上显示出潜力，但在测试场景中，其在预测有限分辨率CFD的密度和压力差异方面的准确性与AD-CNN相当或略差。 AI

影响这项研究引入了一种新颖的无数据流体动力学模拟方法，有望减少该领域AI模型训练对大型数据集的依赖。

排序理由该集群包含一篇详细介绍流体动力学模拟新方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Ryosuke Yano · 2026-10-23 04:00

Data-Free Finite-Volume Convolutional Neural Networks for Steady Compressible Euler Flow around a Circular Cylinder

arXiv:2603.01001v2 Announce Type: replace-cross Abstract: A data-free finite-volume convolutional neural network (FV-CNN) is formulated for steady Euler flow around a cylinder at eight Mach numbers, 2--15. Shared-face fluxes define conservative spatial balances, while automatic d…

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Data-Free Finite-Volume Convolutional Neural Networks for Steady Compressible Euler Flow around a Circular Cylinder

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