A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions
- DOI
- 10.1186/s13104-025-07142-1
- Published
- 2025-02-19
- Container
- BMC Research Notes
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13104-025-07142-1,
title = {A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions},
author = {Alemayehu Tamirie Deresse and Alemu Senbeta Bekela},
year = {2025},
journal = {BMC Research Notes},
doi = {10.1186/s13104-025-07142-1},
url = {https://doi.org/10.1186/s13104-025-07142-1}
}RIS
TY - JOUR TI - A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions AU - Alemayehu Tamirie Deresse AU - Alemu Senbeta Bekela PY - 2025 JO - BMC Research Notes DO - 10.1186/s13104-025-07142-1 UR - https://doi.org/10.1186/s13104-025-07142-1 ER -
APA
Deresse, A. T., & Bekela, A. S. (2025). A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions. BMC Research Notes. https://doi.org/10.1186/s13104-025-07142-1
Source records
- crossref · retrieved 2026-09-26T09:43:03.397Z