English(EN) Neural Operator Splitting for Fluid Dynamics in Complex Geometry

新框架通过算子分解改进流体动力学学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一个名为神经算子分解的新框架，以改进复杂几何形状中流体动力学的学习。该方法将每个时间步分解为单独的、物理上可解释的步骤，每个算子单独学习。该方法利用条件线性傅里叶神经算子 (CL-FNO)，并结合了对称采样和校正循环等策略，以最小化预测过程中的误差累积。实验表明，与单步神经算子相比，滚动误差显著降低，并且推理速度比传统的蒙特卡洛求解器快约 1000 倍。 AI

影响加速流体动力学模拟，从而在天气预报和工程设计等领域实现更快的研发。

排序理由详细介绍流体动力学模拟新方法的学术论文。[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) · Jianxiang Li, Xi Deng, Hong Chul Nam, Anima Anandkumar · 2026-10-21 04:00

用于复杂几何流体动力学的神经算子分解

arXiv:2610.27671v1 Announce Type: new Abstract: Solving dynamical systems in complex and diverse geometries remains challenging for both numerical solvers and learning-based surrogate models. We propose Neural Operator Splitting, a framework for learning fluid dynamics while pres…

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用于复杂几何流体动力学的神经算子分解

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