...; P r = Prandtl number; R e X ˇ = Reynolds number; LMS-ANN = Levenberg–Marquardt Scheme-Artificial Neural Network; MHD = magnetohydrodynamics; MSE = mean square error; α = thermal diffusivity ( m 2 / s ) ; C , c...

... on velocity and thermal profiles are demonstrated graphically. In addition, the artificial neural network ( ANN ) was trained using the Levenberg–Marquardt optimization algorithm to predict the heat transfer rate at the surface of the plate. Findings The outcomes of the present analysis stated...

... the effectiveness of gradient-enhanced physics-informed neural networks (gPINNs) in solving this model, with the goal of improving precision and computational efficiency. Design/methodology/approach The research introduces a coupled system of partial differential equations (PDEs) that describes the chemotaxis...

... algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low...

... database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters...

...Myeongjin Kim; Joo Hyun Moon Purpose This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance. Design/methodology/approach A total of 2,160 computational fluid dynamics simulation cases over...