English(EN) LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics

LSR-Net架构学习非线性流体动力学演化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员推出LSR-Net，这是一种新颖的神经算子架构，旨在模拟非线性流体动力学的正向演化。该网络通过将积分核分解为长程和短程分量，有效地从初始状态和未来状态快照中学习演化算子。短程分量使用卷积处理局部动力学，而长程分量则利用指数和表示来高效计算全局交互。在Burgers方程和浅水方程等基准问题上的评估表明，LSR-Net在预测精度上优于FNO和DeepONets等现有方法。 AI

影响这种新架构有望提高模拟复杂物理系统的准确性和效率。

排序理由该集群包含一篇详细介绍用于科学计算的新神经网络架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Qian Hou, Sutrisno, Yuqing Li, Zecheng Gan · 2026-09-17 04:00

LSR-Net：学习非线性流体动力学的正向演化算子

arXiv:2609.19039v1 Announce Type: cross Abstract: We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling, and extends it to the prediction of nonlinear fluid dynamics. LSR-Net learns the…

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LSR-Net：学习非线性流体动力学的正向演化算子

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