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In this paper, we are interested in the acceleration of numericalsimulations. We focus on a hypersonic planetary reentry problem whosesimulation involves coupling fluid dynamics and chemical reactions. Simulatingchemical reactions takes most of the computational time but, on the other hand,cannot be avoided to obtain accurate predictions. We face a trade-off betweencost-efficiency and accuracy: the simulation code has to be sufficientlyefficient to be used in an operational context but accurate enough to predictthe phenomenon faithfully. To tackle this trade-off, we design a hybridsimulation code coupling a traditional fluid dynamic solver with a neuralnetwork approximating the chemical reactions. We rely on their power in termsof accuracy and dimension reduction when applied in a big data context and ontheir efficiency stemming from their matrix-vector structure to achieveimportant acceleration factors ($\times 10$ to $\times 18.6$). This paper aimsto explain how we design such cost-effective hybrid simulation codes inpractice. Above all, we describe methodologies to ensure accuracy guarantees,allowing us to go beyond traditional surrogate modeling and to use these codesas references.

Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)