A machine learning approach to calculating the non-equilibrium diffusion coefficients in the state-to-state solution of the Navier-Stokes equations

DOI10.1134/s1995080223010213OpenAlexW4377003421MaRDI QIDQ6040353

Elena Mikhailova, Pavel Kiva, Natalia Grafeeva

Publication date: 25 May 2023

Published in: Lobachevskii Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s1995080223010213

zbMATH Keywords

Navier-Stokes equationsneural networksk-NNPINNs

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Parabolic equations and parabolic systems (35Kxx) Astronomy and astrophysics (85-XX)

Cites Work

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• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• KAPPA: kinetic approach to physical processes in atmospheres library in C++

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