In this study, a novel application of backpropagated Levenberg-Marquardt neural networks (LM-NN) based computational intelligent heuristics is presented to interpret the analysis of Maxwell nanofluid thin film flow over a stretchable and rotating disk by considering magnetic and non-linear thermal radiation effects. Utilizing the Buongiorno model, thermophoretic and Brownian motion features of nanofluid are captured. The mathematical model in terms of partial differential equations (PDEs) is reduced to ordinary differential equations (ODEs) by incorporating the similarity transformations. The Adams numerical technique is utilized for generation of a dataset for proposed LM-NN in case of sundry scenarios of TFFPMN by variation of Deborah number, thermophoresis number, Schmidt number, radiation and Brownian motion variables. The training, testing and validation of the intelligent solver LM-NN is performed to find the solution of TFFPMN for various scenarios. Comparison with standard solution verified the precision of LM-NN scheme for the solution of TFFPMN model through mean square error based figure of merit, regression analysis, absolute error analysis and histograms. It is found that radial and azimuthal velocities are decaying functions of Deborah number. Further the nanofluid temperature enhances against higher radiation and thermophoresis parameters. The comparative assessment is performed to validate the present results.

在这项研究中，提出了一种基于反向传播的Levenberg-Marquardt神经网络（LM-NN）的计算智能启发法的新应用，以通过考虑磁性和非线性热辐射来解释麦克斯韦纳米流体薄膜在可拉伸和旋转磁盘上的流动分析。效果。利用Buongiorno模型，可以捕获纳米流体的热泳和布朗运动特征。通过合并相似变换，将偏微分方程（PDE）方面的数学模型简化为常微分方程（ODE）。通过改变Deborah数，热泳数，Schmidt数，辐射和布朗运动变量，在TFFPMN的各种情况下，利用Adams数值技术来为拟议的LM-NN生成数据集。培训，对智能求解器LM-NN进行测试和验证，以找到针对各种情况的TFFPMN解决方案。与标准解的比较通过基于均方误差的品质因数，回归分析，绝对误差分析和直方图验证了LM-NN方案对TFFPMN模型解的精度。发现径向速度和方位角速度是Deborah数的衰减函数。此外，纳米流体温度针对较高的辐射和热泳参数而提高。进行比较评估以验证当前结果。与标准解的比较通过基于均方误差的品质因数，回归分析，绝对误差分析和直方图验证了LM-NN方案对TFFPMN模型解的精度。发现径向速度和方位角速度是Deborah数的衰减函数。此外，纳米流体温度针对较高的辐射和热泳参数而提高。进行比较评估以验证当前结果。与标准解的比较通过基于均方误差的品质因数，回归分析，绝对误差分析和直方图验证了LM-NN方案对TFFPMN模型解的精度。发现径向速度和方位角速度是Deborah数的衰减函数。此外，纳米流体温度针对较高的辐射和热泳参数而提高。进行比较评估以验证当前结果。