Intelligent computing for electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification: Levenberg–Marquardt backpropagation algorithm
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Date
2024-03
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AIP Advances
Abstract
The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to
perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with
thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help
of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of
a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a
typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation
purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized
for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts
and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square
error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data
based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid
function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the
learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from
106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm.
Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram
are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for
fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization
of fluid systems, resulting in increased efficiency and production across various technical domains.
Description
This article is published by AIP Advances 2024 and is also available at 10.1063/5.0187124
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Citation
AIP Advances 14, 035224 (2024)