A priori neural networks versus a posteriori MOOD loop: a high accurate 1D FV scheme testing bed

DOI10.1007/s10915-020-01282-1zbMath1450.65093OpenAlexW3044951046MaRDI QIDQ2007012

Raphaël Loubère, Alexandre Bourriaud, Rodolphe Turpault

Publication date: 12 October 2020

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-020-01282-1

zbMATH Keywords

neural networkfinite volume schememachine learninghyperbolic systemhigh accuracya posteriori MOOD

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Error bounds for initial value and initial-boundary value problems involving PDEs (65M15) Applications to the sciences (65Z05) Radiative transfer in astronomy and astrophysics (85A25) Finite volume methods for initial value and initial-boundary value problems involving PDEs (65M08)

Related Items (3)

Hyperbolic balance laws: modeling, analysis, and numerics. Abstracts from the workshop held February 28 -- March 6, 2021 (hybrid meeting) ⋮ Deterministic neural networks optimization from a continuous and energy point of view ⋮ GP-MOOD: a positivity-preserving high-order finite volume method for hyperbolic conservation laws

Uses Software

• MOOD

Cites Work

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