Yiqi Feng

Yiqi Feng is a doctoral researcher at the Chair of Aerodynamics and Fluid Mechanics at Technical University of Munich, working under the supervision of Prof. Dr.-Ing. Nikolaus Adams. Currently completing their dissertation titled "Data-driven Methods on Optimizing Numerical Schemes for Complex Compressible Flows" with expected completion in 2025.

Research focuses on the integration of machine learning techniques with computational fluid dynamics, specifically developing optimization frameworks for numerical schemes in compressible flow simulations. Key interests include Bayesian optimization, deep reinforcement learning applications, and high-order numerical methods for complex flow phenomena. The work bridges traditional fluid mechanics with modern data-driven approaches to enhance simulation accuracy and efficiency.

Publications demonstrate a consistent research trajectory in developing adaptive optimization frameworks for fluid dynamics simulations, with increasing sophistication from 2022-2024. The research shows strong emphasis on multi-objective optimization techniques applied to numerical scheme design, particularly for handling compressible flows with complex shock structures and discontinuities.

As part of TUM's Aerodynamics and Fluid Mechanics research group, the work contributes to the department's broader initiatives in flow simulation, wind tunnel testing, and computational methods development, supporting projects like CRC and FURADO mentioned in the institutional research structure.