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Machine learning approximations in finite volume methods

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The Riemann solver is the foundation of many finite volume methods used in computational fluid dynamics (CFD). In this talk we discuss how a some of the approximations in common Riemann solvers can be improved: by constructing a neural network to estimate some of the physical quantities required to construct the approximate solution of the Riemann problem, we can achieve better performance both in accuracy and in time-to-solution.

This talk is part of the RSE Seminars series.

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