Discovering artificial viscosity models for discontinuous Galerkin approximation of conservation laws using physics-informed machine learning
DOI10.1016/J.JCP.2024.113476MaRDI QIDQ6648381
Luca Dedé, Paola F. Antonietti, Matteo Caldana
Publication date: 4 December 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
conservation lawsartificial viscosityneural networksreinforcement learningdiscontinuous Galerkinphysics-informed machine learning
Hyperbolic conservation laws (35L65) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) General topics in artificial intelligence (68T01)
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