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Updated: Jun 26, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
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基于人工智能的预测方法通过FFB在Ostwald de-Waele流体的驱动腔内传播,使用CFD-ANN和Levenberg-Marquardt

Ahmed Refaie Ali1, Rashid Mahmood2, Atif Asghar2

  • 1Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El Kom, 32511, Menofia, Egypt. ahmed.refaie@science.menofia.edu.eg.

Scientific reports
|May 14, 2024
PubMed
概括

这项研究将计算流体动力学 (CFD) 与人工神经网络 (ANN) 结合起来,以准确预测流体对障碍物的力. 混合方法显著减少了计算时间,同时保持了对拖动和提升系数的高预测准确性.

关键词:
人工智能 (AI) 是一种人工智能.人工神经网络 (ANN) 是一个人工神经网络.驱动的空洞驱动的空洞有限元素方法 (FEM)混合型的CFD-ANN可以提供.莱文伯格马奎特算法 莱文伯格马奎特算法奥斯瓦尔德·德·韦尔的流体流体

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