Approximation of One-Dimensional Darcy–Brinkman–Forchheimer Model by Physics Informed Deep Learning Feedforward Artificial Neural Network and Finite Element Methods: A Comparative Study

In the last few years, a new research program such as deep learning neural networks (or simulated neural networks )—a class of machine learning algorithms—has gained a lot of attention due to its applicability in various science and engineering fields. From the numerical methods point of view, in re...

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Bibliographic Details
Published in:	International journal of applied and computational mathematics Vol. 10; no. 3; p. 102
Main Authors:	Martinez, Mara, Rao, B. Veena S. N., Mallikarjunaiah, S. M.
Format:	Journal Article
Language:	English
Published:	New Delhi Springer India 01.06.2024 Springer Nature B.V
Subjects:	Accuracy Algorithms Applications of Mathematics Approximation Artificial neural networks Boundary conditions Boundary value problems Brinkman model Comparative studies Computational mathematics Computational Science and Engineering Datasets Deep learning Differential equations Finite element analysis Finite element method Finite volume method Galerkin method Machine learning Mathematical analysis Mathematical and Computational Physics Mathematical Modeling and Industrial Mathematics Mathematical models Mathematics Mathematics and Statistics Neural networks Nuclear Energy Numerical analysis Numerical methods Operations Research/Decision Theory Optimization Ordinary differential equations Original Paper Porous media Sensitivity analysis Theoretical Unsupervised learning Viscosity Finite element method Unsupervised machine learning Darcy–Brinkman–Forchheimer model Activation function Optimizer Network sensitivity
ISSN:	2349-5103, 2199-5796
Online Access:	Get full text
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