English(EN) Local and Sparse Kernel Operator Learning for Incompressible Flows: An Operator Lagrange Formulation

新的稀疏核方法推动不可压缩流算子学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员开发了一种新的局部和稀疏核方法，用于学习求解不可压缩流问题的算子。该方法通过使用局部无散插值和紧支撑核来改进先前的方法，从而实现稀疏算子回归。该技术可以在 GPU 上并行化，与神经算子等现有方法相比，在复杂流体动力学模拟方面，误差显著降低，精度相当或更高。 AI

影响该方法可以通过提高流体动力学模拟的效率和准确性来加速科学发现。

排序理由详细介绍科学计算新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ramansh Sharma, Matthew Lowery, Houman Owhadi, Varun Shankar · 2026-10-22 04:00

不可压缩流动的局部与稀疏核算子学习：算子拉格朗日表述

arXiv:2610.29009v1 Announce Type: new Abstract: We present a local and sparse kernel method for learning the solution operators of incompressible flows. It builds on our earlier property-preserving kernel method, whose global output reconstruction and dense operator regression sc…