English(EN) Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction

ScaleSplit-NO 神经算子提升 3D 湍流预测效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种名为 ScaleSplit-NO 的新型神经算子，旨在提高 3D 湍流预测的效率。该方法使用两个算子：一个 Parent 模型用于粗粒度预测，一个 Child 模型用于高分辨率局部细节，并以 Parent 的输出为条件。这种方法避免了在全分辨率场上操作，显著降低了内存和数据需求。ScaleSplit-NO 在湍流基准测试中表现出卓越的准确性和数据效率，并已应用于蒙特利尔的城市风力预测。 AI

影响为复杂的模拟引入了一种更节省内存和数据的方法，有可能加速科学发现和城市规划等现实世界应用。

排序理由关于湍流预测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Shaoxiang Qin, Yucheng Zhao, Zongyi Li, Liangzhu Leon Wang, Xiongye Xiao · 2026-10-01 04:00

用于内存和数据高效3D湍流预测的尺度分裂神经算子

arXiv:2609.38977v1 Announce Type: cross Abstract: Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolution remains challenging, since the memory of full-field models grows with the r…

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报道来源 [1]

用于内存和数据高效3D湍流预测的尺度分裂神经算子

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