一种新的机器学习方法,用于分析多相波动流的电宇宙效应和热传递
Muhammad Naeem Aslam1, Arshad Riaz2, Muhammad Sarmad Arshad1
1Department of Mathematics, Lahore Garrison University, Lahore, 54770, Pakistan.
Scientific reports
|October 22, 2025
概括
一个新的混合人工神经网络 (ANN) 模型有效地分析了多相波浪流与霍尔电流和电磁效应. 这种方法准确地捕捉复杂的动态,为流体流动模拟提供可靠的解决方案.
科学领域:
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 在工程领域的人工智能.
- 非线性动力学是一种非线性动力学.
背景情况:
- 用霍尔电流和电磁效应分析多相波流提出了重大的计算挑战.
- 传统的数值方法很难有效地捕捉这种复杂的流体动力学的固有非线性行为.
- 集成先进的计算技术对于准确和强大的流量分析至关重要.
研究的目的:
- 开发和验证一种新的混合方法来分析多相波流,其中包括霍尔电流和电磁效应.
- 引入创新的人工神经网络 (ANN) 架构,Morlet波形Tanh神经网络 (MTNNs),用于增强流动力学建模.
- 通过将结果与物理信息的神经网络 (PINNs) 进行比较,评估拟议的MTNNs的准确性,稳定性和稳定性.
主要方法:
- 支配部分微分方程 (PDEs) 被转换为普通微分方程 (ODEs).
- 开发了一个混合模型,将ANN与启发式算法结合起来,特别是通过粒子群优化 (PSO) 优化的MTNN.
- MTNN使用了一种新的激活函数,它结合了Morlet波形函数和过度触角 (Tanh) 函数.
- 为了验证,使用了带有Adam优化器的物理信息神经网络 (PINNs).
主要成果:
- 拟议的MTNNs表现出高精度,速度和温度的平均平方误差 (MSE) 值在指定的范围内.
- 统计分析证实了MTNNs解决方案的稳定性,融合性和稳定性.
- 流速和热分布被发现直接受到电学因素的影响,而磁场则反过来影响.
- 来自MTNNs的结果与使用PINNs获得的结果非常一致,证实了该模型的有效性.
结论:
- 新的MTNNs混合方法为分析复杂的多相波形流与霍尔电流和电磁效应提供了有效和准确的方法.
- 在ANN中,Morlet波段和Tanh函数作为激活函数的集成成功地捕获了非线性流动.
- 该研究验证了拟议的MTNNs作为一种可靠的替代方案,用于类似的流体动力学问题的现有数值方法,如PINNs.
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