Applications: Combustion, Materials, Turbulent Flow Methodologies: Large-scale Inverse Problems, Machine Learning Relevant Projects: NASA, DOE, NSF, GE, NAVY

Karthik Duraisamy

Assistant Professor, Aerospace Engineering

Prof. Duraisamy’s group focuses on data-driven modeling of computational physics problems. Specifically, we use statistical inversion and physics-informed machine learning techniques to augment existing computational models. Another focus area is formal reduced order modeling using data-driven decompositions.

Our application areas are in turbulence, combustion and materials physics.

Turbulent flow in a trailing vortex.

Turbulent flow in a trailing vortex.