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Structure-Preserving Algorithms

KAUST-CEMSE-Matteo-Parsani

Matteo Parsani

Associate Professor, Applied Mathematics and Computational Science

Structure-Preserving Algorithms fluid dynamics High Performance Computing Numerical Optimization aeroacoustics

Professor Parsani develops robust, variable order and self-adaptive algorithms for solving fluid flow problems in complex geometries. The application domains currently driving Parsani's research are compressible computational aerodynamics, computational aeroacoustics for noise reduction in vehicles and molecular communication.

Bayesian Deep Learning (BDL)

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