My research group develops models and algorithms for large-scale inverse problems, especially image reconstruction for X-ray CT and MRI. The models include those based on sparsity using dictionaries learned from large-scale data sets. Developing efficient and accurate methods for dictionary learning is a recent focus.
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.