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

DOI10.1016/j.jcp.2023.112700arXiv2209.13434OpenAlexW4297949954MaRDI QIDQ6187668

Gaël Poëtte, Pietro Marco Congedo, Paul Novello, David Lugato, S. Peluchon

Publication date: 31 January 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2209.13434

zbMATH Keywords

chemical reactionshybridizationdeep neural networksreentryscientific machine learning

Mathematics Subject Classification ID

Numerical methods for partial differential equations, boundary value problems (65Nxx) Probabilistic methods, stochastic differential equations (65Cxx) Approximations and expansions (41Axx)

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