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Matteo Parsani

Assistant Professor
Department of Applied Mathematics and Computational Science
King Abdullah University of Science and Technology
Saudi Arabia

Biography

Prof. MATTEO PARSANI Specialises in Computational Science. He is working as Professor Department of Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Saudi Arabia.

Research Interest

​Professor Matteo Parsani’s research interests are related to the design and implementation of novel, robust, and scalable numerical methods on unstructured grids for hyperbolic and mixed hyperbolic/parabolic partial differential equations, as well as their application to solve realistic flow problems in various areas of natural science and engineering. To this end, he combines numerical analysis, physics, and high performance computing to develop robust spatial and temporal discretizations with better mathematical and numerical properties that account for the physical phenomena being modeled and the modern computing architectures. Application domains that are currently driving his research are computational aero and gas dynamics, dense gas flows simulations, and computational aeroacoustics. Professor Parsani’s research interests at KAUST will focus on the design and implementation of non-linearly stable, high-order adaptive numerical methods for solving industrial hitherto intractable multi-scale flow problems (e.g., highly-separated turbulent flows with LES and DNS; and supercritical fluids at different flow regimes) on hundreds of thousands of cores and the emerging heterogeneous data-centric computing hardware (CPUs + GPUs).

Publications

  • Parsani, M., Carpenter, M. H., and Nielsen, E. J. Entropy stable discontinuous interface coupling for the three-dimensional compressible Navier-Stokes equations, Journal of Computational Physics, 290(1): 132-138, 2015.

  • Parsani, M., Carpenter, M. H., and Nielsen, E. J. Entropy stable wall boundary conditions for the three-dimensional compressible Navier-Stokes equations, Journal of Computational Physics, 292(1): 88-113, 2015.

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