Dongbin Xiu

Dongbin Xiu's research focuses on building mathematical and machine-learning tools to model, predict, and control complex systems whose governing equations are unknown or only partially known, such as fluid flows and other dynamical systems. Much of the work combines numerical methods (like polynomial approximation and uncertainty quantification) with deep learning techniques (such as flow map learning and diffusion models) to create data-driven simulations, digital twins, and parameter estimation methods. This work is often motivated by engineering applications like fluid dynamics and flow control, but also touches on general-purpose tools for scientific computing.

Publication output has remained fairly steady over the last decade, averaging about 9-10 papers per year with a modest uptick in 2024.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026