I’m a PhD student at the University of Washington, working with professor Steve Brunton. My research is focused on AI for computational fluid dynamics prediction and control.
I am interested in leveraging advanced data-driven methods to improve the engineering design process. Specifically, I have been exploring the use of machine learning methods and reinforcement learning for predicting and controlling complex flow environments, such as extreme event turbulence. I am also involved in the development of differentiable flow solvers to enable gradient-based optimization for tasks such as shape optimization or flow control. I am also passionate about developing open-source software to further drive data-driven discovery of dynamics.
Sajeda Mokbel*, Christian Lagemann, Esther Lagemann, Steven L. Brunton. Opensource package + paper coming soon.
Christian Lagemann, Ludger Paehler, Jared Callaham, Sajeda Mokbel*, Samuel Ahnert, Kai Lagemann, Esther Lagemann, Nikolaus Adams, Steven Brunton. PMLR (2025).
Sajeda Mokbel*, Christian Lagemann, Esther Lagemann, Steven L. Brunton. Controlling chaotic energy events in fluids with reinforcement learning (2025).