Hybrid nanofluid is an emerging field due to the rapid enhancement of heat transfer and stable nanoparticles in base fluid properties. A three dimensional hybrid nanofluid flow model is constructed over biaxial porous stretching/shrinking sheet with heat transfer, Radiative heat and mass flux (3D-HNF-RHF). Bayesian Regularization technique based on Backpropagated neural networks (BRT-BNNs) is employed to estimate the solution of proposed model. It is the mathematical process which converts nonlinear regression fitting into a well-posed statistical process in the form of ridge regression. This method diminishes length cross validation need and more robust then Backpropagation technique. The proposed flow system 3D-HNF-RHF is transferred to ordinary differential equations (ODEs) possessing physical variations through self-similar transformations. The effect of derived variations such as thermal relaxation parameter, mass flux parameter, Stretching/shrinking parameter, Prandtl number, Skin friction and Nusselt number observed over the velocity and temperature fields. Numerical results of these physical parameters have been presented in tabulated form obtained from a dataset constructed through Homotopy Analysis Method imposed on 3D-HNF-RHF model. Statistical tests through mean square error, histogram curve and regression fitting curves are employed to check the accuracy and convergences of the solution obtained through BRT-BNNs. It is observed that Stretching/shrinking quantity and mass flux parameter slow down the flow rate whereas, increase radiative heat flux upsurges the temperature gradient. The impacts of surface drag force and heat transfer are illustrated through the different graphical illustrations.

由于热传递的快速增强和基础流体特性中纳米粒子的稳定，混合纳米流体是一个新兴领域。在具有传热、辐射热和质量通量 (3D-HNF-RHF) 的双轴多孔拉伸/收缩片上构建了三维混合纳米流体流动模型。采用基于反向传播神经网络（BRT-BNNs）的贝叶斯正则化技术来估计所提出模型的解。它是将非线性回归拟合转化为岭回归形式的适定统计过程的数学过程。这种方法减少了长度交叉验证的需要，并且比反向传播技术更强大。所提出的流动系统 3D-HNF-RHF 通过自相似变换转换为具有物理变化的常微分方程 (ODE)。在速度和温度场上观察到的衍生变化的影响，例如热弛豫参数、质量通量参数、拉伸/收缩参数、普朗特数、皮肤摩擦和努塞尔数。这些物理参数的数值结果已以表格形式呈现，这些结果是从通过强加在 3D-HNF-RHF 模型上的同伦分析方法构建的数据集获得的。通过均方误差、直方图曲线和回归拟合曲线进行统计检验，以检查通过 BRT-BNN 获得的解的准确性和收敛性。据观察，拉伸/收缩量和质量通量参数会减慢流速，而增加辐射热通量会使温度梯度升高。