Heng Xiao

Heng Xiao is a Professor of Data-Driven Fluid Dynamics at the University of Stuttgart, affiliated with the Institute of Aerospace Thermodynamics (ITLR) and the Cluster of Excellence EXC 2075 'Data-Integrated Simulation Science' in the Stuttgart Center for Simulation Science (SC SimTech). He previously served as Associate Professor (2020-2022) and Assistant Professor (2013-2020) at Virginia Tech, USA, and was a Postdoctoral Researcher/Lecturer at ETH Zürich (2009-2012).

• Ph.D., Civil Engineering, Princeton University, 2009
• M.S., Scientific Computing, Royal Institute of Technology (KTH), 2005
• B.S., Civil Engineering, Zhejiang University, 2003

His research focuses on integrating data science (machine learning, uncertainty quantification, data assimilation) with traditional physical models to advance predictive capabilities in multi-scale fluid systems. Key areas include Data-Driven Turbulence Modeling, Laminar-Turbulent Transition, Subsurface Flows, and Particle-Laden Flows. His work addresses turbulence modeling through neural operators, Bayesian inference, and physics-informed machine learning, with applications in aerospace, ocean engineering, and geosciences.

Recent publications highlight trends in Neural Operators for Nonlocal Models, Ensemble Kalman Methods for Turbulence Inference, and Machine Learning for Permeability Prediction. Collaborative projects, such as the DFG-funded development of coupled turbulence and heat-flux models for film cooling, underscore his focus on real-world impact.

• Fellowship, Center of Turbulence Research Summer Program, Stanford University (2016)
• Finalist, Undergraduate Research Advisor Award, Virginia Tech (2014)

Advisor to doctoral students including Jian-Xun Wang, Rui Sun, Jin-Long Wu, and Carlos Michelén-Ströfer, he leads the 'Data-Driven Fluid Dynamics' group at Stuttgart. The team collaborates with academia and industry, emphasizing high-performance computing and open-source tools like SediFoam for sediment transport simulations.