English(EN) Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks

可微分物理和PINN重构壁面剪切应力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-04 15:53

研究人员开发了两种逆向框架，即可微分物理方法和物理信息神经网络（PINNs），以从被动标量观测中重构壁面剪切应力（WSS）。该研究在经典和患者特异性心血管流动问题上评估了这些方法。与PINNs相比，可微分物理框架在各种测量场景下表现出更高的准确性和鲁棒性，尤其是在近壁数据有限的情况下。 AI

影响这项研究可以从标量传输数据中实现更准确的血液动力学分析，从而可能改善心血管诊断。

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arXiv cs.LG TIER_1 English(EN) · Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza, Amirhossein Arzani · 2026-06-05 04:00

从浓度重构壁面剪切应力：可微分物理与物理信息神经网络

arXiv:2606.06313v1 Announce Type: cross Abstract: Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise computation of near-wall velocity gradi…
arXiv cs.LG TIER_1 English(EN) · Amirhossein Arzani · 2026-06-04 15:53

从浓度重构壁面剪切应力：可微分物理与物理信息神经网络

Wall shear stress (WSS) governs near-wall transport dynamics and is a key hemodynamic indicator in cardiovascular flows, yet remains difficult to infer accurately due to the need for precise computation of near-wall velocity gradients. Passive scalar fields, such as concentration…