Solving a fourth order partial differential equations using deep neural networks
| dc.contributor.author | Francis, J.M. | |
| dc.contributor.author | Godavarma, C. | |
| dc.date.accessioned | 2026-02-06T06:33:37Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | 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. © 2024 Author(s). | |
| dc.identifier.citation | AIP Conference Proceedings, 2024, Vol.3081, 1, p. - | |
| dc.identifier.issn | 0094243X | |
| dc.identifier.uri | https://doi.org/10.1063/5.0196097 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/28755 | |
| dc.publisher | American Institute of Physics | |
| dc.title | Solving a fourth order partial differential equations using deep neural networks |