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- W3204093067 abstract "This article discussed the influence of activation energy on MHD flow of third-grade nanofluid model (MHD-TGNFM) along with the convective conditions and used the technique of backpropagation in artificial neural network using Levenberg-Marquardt technique (BANN-LMT). The PDEs representing (MHD-TGNFM) transformed into the system of ODEs. The dataset for BANN-LMT is computed for the six scenarios by using the Adam numerical method by varying the local Hartman number (Ha), Prandtl number (Pr), local chemical reaction parameter (σ), Schmidt number (Sc), concentration Biot number (γ2) and thermal Biot number (γ1). By testing, validation and training process of (BANN-LMT), the estimated solutions are interpreted for (MHD-TGNFM). The validation of the performance of (BANN-LMT) is done through the MSE, error histogram and regression analysis. The concentration profile increases when there is an increase in Biot number and the local Hartmann number; meanwhile, it decreases for the higher values of Schmidt number and the local chemical reaction parameter." @default.
- W3204093067 created "2021-10-11" @default.
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- W3204093067 date "2021-09-29" @default.
- W3204093067 modified "2023-10-16" @default.
- W3204093067 title "Intelligent Computing with Levenberg–Marquardt Backpropagation Neural Networks for Third-Grade Nanofluid Over a Stretched Sheet with Convective Conditions" @default.
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- W3204093067 doi "https://doi.org/10.1007/s13369-021-06202-5" @default.
- W3204093067 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8479501" @default.
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- W3204093067 hasPublicationYear "2021" @default.
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