Machine learning moment closure models for the radiative transfer equation. I: Directly learning a gradient based closure

DOI10.1016/j.jcp.2022.110941OpenAlexW3161283735WikidataQ111521731 ScholiaQ111521731MaRDI QIDQ2135258

Andrew J. Christlieb, Juntao Huang, Luke F. Roberts, Yingda Cheng

Publication date: 4 May 2022

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

Full work available at URL: https://arxiv.org/abs/2105.05690

zbMATH Keywords

moment closureneural networkmachine learningradiative transfer equationslab geometry

Mathematics Subject Classification ID

Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Hyperbolic equations and hyperbolic systems (35Lxx) Time-dependent statistical mechanics (dynamic and nonequilibrium) (82Cxx)

Related Items (4)

Machine learning moment closure models for the radiative transfer equation. III: enforcing hyperbolicity and physical characteristic speeds ⋮ Machine Learning Moment Closure Models for the Radiative Transfer Equation II: Enforcing Global Hyperbolicity in Gradient-Based Closures ⋮ Learning invariance preserving moment closure model for Boltzmann-BGK equation ⋮ Neural-network based collision operators for the Boltzmann equation

Uses Software

• PyTorch

Cites Work

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