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The silver (Ag) and titanium oxide (TiO 2 ) nanoparticle combination is responsible for the uniform heat distribution, which enhances the heat transfer of the hybrid nanofluid (HNF). The HNFs are capable of enhancing heat transfer, which has significant applications in energy resources and heat exchangers. The flow is the result of the convergence and divergence of two non-parallel Riga walls. The control volume finite element method is employed to obtain a microscopic view of the HNFs between non-parallel rigid walls. Furthermore, the governing equations of the physical system are solved using a machine learning technique called wavelet-based physics-informed neural network. The methodology’s implementation is flexible due to the nonlinearity of the equations, and the activation function used enhances the accuracy of the solution. Enhancement of the heat transfer rate is obtained by varying the embedded parameters. The percentage increase in heat transfer rate is achieved from 30.7 to 24.3% using the Ag and TiO 2  HNFs. The fuzzy inference technique function outputs, mean squared error results, and error normalized squared error are observed with the best validation employing the neural network. In the end, the results are compared with the existing literature.

A titanium oxide- and silver-based hybrid nanofluid flow between two Riga walls that converge and diverge through a machine-learning approach