We are pleased to announce the success of our student enrolled in the Computational Science and Engineering PhD program. On January 12, 2026, Afrah Farea successfully defended her PhD thesis titled "Quantum Machine Learning Algorithms for the Solution of Partial Differential Equations."
Dr. Farea’s work connects two powerful fields: Quantum Computing and Artificial Intelligence. During her studies, she developed new algorithms to solve complex physical problems.
We congratulate Dr. Farea on this well-deserved success and look forward to seeing her future contributions to science. Related resources and publications can be found below.
Selected Publications:
- Farea, A., Khan, S., & Çelebi, M.S. (2025). QCPINN: quantum-classical physics-informed neural networks for solving PDEs. Machine Learning: Science and Technology, 6, 045053. https://iopscience.iop.org/article/10.1088/2632-2153/ae1c91
- Farea, A., & Çelebi, M.S. (2025). Learnable activation functions in physics-informed neural networks for solving partial differential equations. Computer Physics Communications, 315, 109753. https://doi.org/10.1016/j.cpc.2025.109753
In the News:
Her Github repository can be found at
https://github.com/afrah/