I’m Hamza Kamil, a PhD candidate in a cotutelle program between École de Technologie Supérieure (Montréal, Canada) and University Mohammed VI Polytechnic (Ben Guerir, Morocco). My work sits at the intersection of applied mathematics and scientific machine learning, with two main thrusts:

1. Theory of Scientific ML, where I investigate the mathematical foundations and training dynamics of these algorithms to improve their efficiency and reliability.
2. Physics-Informed Neural Networks (PINNs) for modeling coupled multiphysics processes in real-world environments (e.g., surface–subsurface water and nutrient transport in soils).

I’m fortunate to be advised by Prof. Azzeddine Soulaïmani and Prof. Abdelaziz Beljadid.

Recent Highlights

• “Physics-informed neural networks for solving moving interface flow problems using the level set approach”
  Mathieu, M., Kamil, H., Fahsi, A., Soulaïmani, A. Physics of Fluids, Article 107124 (2025).

• “A comparative study of physics-informed neural network strategies for modeling water and nitrogen transport in unsaturated soils”
  Kamil, H., Soulaïmani, A., Beljadid, A. Journal of Hydrology, Article 133624 (2025).

• “Physics-informed neural operators for efficient modeling of infiltration in porous media”
  Kamil, H., Soulaïmani, A., Beljadid, A. Journal of Computational Physics, Article 114156 (2025).