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Jeff Fessler

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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.

For a summary of how model-based image reconstruction methods lead to improved image quality and/or lower X-ray doses, see: http://web.eecs.umich.edu/~fessler/re

For a summary of how model-based image reconstruction methods lead to improved image quality and/or lower X-ray doses, see: http://web.eecs.umich.edu/~fessler/re

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Karthik Duraisamy

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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.