An innovative idea of regression analysis based on machine learning technique for magnetohydrodynamic flow of Maxwell fluid within a cylinder is proposed. Mean Squared Error is used for the simulation of heat transfer and fluid flow. The governing flow equations involving a system of coupled, nonlinear fractional partial differential equations are solved by homotopic approach called HPM. The predicted solution is obtained with Python built-in code on Google-Colab. The effects of Atangana-Baleanu fractional time order derivative on the momentum, thermal, and concentration boundary layer are analyzed. It is observed that the momentum boundary layer gets higher and higher by increasing the values of Atangana-Baleanu fractional time order derivative. The thermal boundary layer shows improvement with the increasing value of the Peclet number. The concentration boundary layer thickness declines with the growing values of chemical reactions. The validation of results is examined by MSE, function fit, and correlation index.

中文翻译：

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基于集成机器学习方法的化学反应影响下分数阶磁流体动力学麦克斯韦流体流动热传递的回归分析


提出了一种基于机器学习技术的圆柱体内麦克斯韦流体磁流体动力学流回归分析的创新思路。均方误差用于模拟传热和流体流动。涉及耦合非线性分数偏微分方程组的控制流方程通过称为 HPM 的同位方法求解。预测的解是通过 Google-Colab 上的 Python 内置代码获得的。分析了 Atangana-Baleanu 分数时间阶导数对动量、热和浓度边界层的影响。据观察，通过增加 Atangana-Baleanu 分数时间阶导数的值，动量边界层越来越高。热边界层随着 Peclet 数值的增加而有所改善。浓度边界层厚度随着化学反应值的增长而减小。通过 MSE 、 函数拟合 和 相关指数 检验结果的验证。