Physics-Informed Neural Networks for Solving Bagley-Torvik fractional differential equation
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32833Keywords:
Bagley Torvik fractional differential equationAbstract
The Physics-Informed Neural Network (PINN) scheme is introduced in this work to obtain numerical solutions for the Bagley–Torvik type of fractional differential equation. The proposed algorithm generates solutions in the form of a rapidly converging sequence, ensuring computational efficiency and stability. To evaluate the accuracy, robustness, and convergence of the method, several illustrative examples are presented. The comparative analysis shows that the PINN-based scheme offers highly accurate approximations and demonstrates strong reliability across a wide range of nonlinear problems, making it an effective approach for solving complex fractional differential systems.
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Copyright (c) 2026 Zena Talal Yassin, Dalya Mahmood Merie, Waleed Al-Hayani
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