English(EN) Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models

新方法改进了流体动力学模拟中的神经网络性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种方法来改进神经网络亚网格应力模型的性能，这些模型常用于大涡模拟（LES）。与先验评估相比，这些模型在事后 LES 应用中通常会显示出显著的性能下降。所提出的解决方案包括使用两种不同的滤波器来增强训练数据，并降低神经网络输入的复杂性。这种组合方法能够实现更稳健的事后性能，更好地反映先验评估结果，从而使模型在 LES 应用中更加可靠。 AI

影响这项研究可能带来更准确、更可靠的流体动力学模拟，影响依赖此类建模的领域。

排序理由学术论文，详细介绍了一种改进神经网络模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Andy Wu, Sanjiva K. Lele · 2026-09-11 04:00

解决神经网络亚网格应力模型事后性能下降问题

arXiv:2511.17475v2 Announce Type: replace-cross Abstract: Neural network subgrid stress models often have a priori performance that is far better than the a posteriori performance, leading to neural network models that look very promising a priori completely failing in a posterio…

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解决神经网络亚网格应力模型事后性能下降问题

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