English(EN) Eikonal Regularisation in Physics-Informed Neural Networks for Three-Dimensional Level-Set Advection: Transferability of Two-Dimensional Design Principles

探索用于3D流体动力学的PINN中的程函方程正则化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

一篇新研究论文探讨了程函方程正则化在物理信息神经网络（PINNs）中模拟三维流体动力学的有效性。该研究由Muhammad Akbar Khan撰写，研究了该正则化器的权重如何影响各种基准测试的准确性和可复现性，包括平移、旋转和带槽球体。研究结果表明，最佳权重高度依赖于流动的几何复杂性，并且尽管PINNs提供了一种有前途的方法，但在测试场景中，五阶WENO求解器表现出更高的准确性。 AI

影响这项研究可能通过AI在流体动力学中实现更准确和可复现的模拟。

排序理由该集群包含一篇详细介绍神经网络在特定科学问题中的新颖应用的论文。[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) · Muhammad Akbar Khan · 2026-08-11 04:00

物理信息神经网络中用于三维水平集平流的Eikonal正则化：二维设计原则的可迁移性

arXiv:2608.08322v1 Announce Type: cross Abstract: Physics-informed neural networks applied to the level-set formulation of interface advection commonly augment the residual and initial-condition losses with an eikonal regulariser, penalising the deviation of $\|\nabla\phi\|$ from…

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物理信息神经网络中用于三维水平集平流的Eikonal正则化：二维设计原则的可迁移性

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