Solving a fourth order partial differential equations using deep neural networks

In this study, we compare the effect of different hyperparameters of the deep neural network while solving partial differential equations. We consider feed-forward neural networks, and the hyperparameters such as the number of hidden layers, number of neurons, activation function, optimization metho...

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Bibliographic Details
Main Authors: Francis, Jain M., Chandhini, G.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Machine learning
Mathematical analysis
Partial differential equations
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Summary:In this study, we compare the effect of different hyperparameters of the deep neural network while solving partial differential equations. We consider feed-forward neural networks, and the hyperparameters such as the number of hidden layers, number of neurons, activation function, optimization methods, and learning rate are studied in this paper. Numerical results for the effect of all the hyperparameters while solving fourth-order partial differential equations are mentioned in this study.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0196097