Deniz Bezgin

Dr. Deniz Bezgin is a Researcher at the Department of Aerodynamics and Fluid Mechanics of the Technische Universität München (TUM). Her work focuses on computational fluid dynamics (CFD), machine learning integration in numerical methods, and high-order differentiable solvers for compressible flows.

• Research specialties include shock-capturing methods, multi-phase flow modeling, and data-driven shape optimization.
• Developed JAX-Fluids, a fully-differentiable framework for compressible two-phase flows.
• Key contributions to ENO/WENO schemes and thermodynamically consistent interface models.
• Current projects explore machine-learned discretizations and GPU-based high-performance computing.

Her recent publications address differentiable simulations, data assimilation, and turbulence modeling. She has not received any explicitly listed scientific awards.