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对于管道流的多个案例物理信息的神经网络的策略:使用2D流场景的研究

Hong Shen Wong¹, Wei Xuan Chan¹, Bing Huan Li²

¹Department of Bioengineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.

Scientific reports

|May 21, 2024

概括

基于物理学的神经网络 (PINNs) 为血管和呼吸道流动提供更快的流体动力学模拟. 使用"模式"网络架构的多个案例方法被证明是最有效的,提高了未见几何形状的准确性和速度.

科学领域:

计算流体动力学计算流体动力学
生物医学工程生物医学工程
机器学习机器学习

背景情况:

管状几何形状的精确流体动力学计算对于生物医学应用至关重要,包括血管和呼吸道分析.
传统的计算流体动力学 (CFD) 方法对于单个流动场景可能耗时.
基于物理学的神经网络 (PINNs) 是一个有希望的替代方案,但通常需要为每一个新案例提供广泛的培训.

研究的目的:

优化多个案例的物理信息神经网络 (PINN) 方法,以在参数化几何中进行高效的流体动力学模拟.
为了比较三种不同的网络架构 (混合网络,超级网络,模式网络) 对多个案例PINN的性能.
调查提高多个案例PINN准确性和计算效率的策略,包括输入参数整合和额外的损失条款.

主要方法:

在理想化的 2D 狭窄管流动场景上评估了三个多个案例的 PINN 架构 (混合网络,超级网络,模式网络).
研究了将几何坐标参数 (距离墙壁,中心线距离) 作为输入的影响.
评估添加额外损失条款 (类似gPINN) 的效果,以执行衍生制约.

主要成果:

多个案例参数PINN方法是可行的,模式网络展示了卓越的准确性,收性和速度.
整合几何坐标参数显著提高了准确性和减少了计算负载.
额外的损失条款提高了混合网络和超级网络的性能,但没有使模式网络受益.

结论:

模式网络架构与几何坐标输入相结合,为多案例流体动力学模拟提供了高效准确的解决方案.
该研究确定了用于将多个案例PINN扩展到更复杂的3D几何形状和多样化的流量条件的关键策略.
这些进展为PINNs在生物医学流体动力学分析中的临床应用铺平了道路.

关键词:

深度学习是一种深度学习.流体流动的流量流动.超级网络超级网络是一种超级网络.基于物理学的神经网络.

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