A block-diagonal symmetrizer and algebraic conditions on closure blocks enable a data-learnable parametrization of ML moment closures for 2D RTE that guarantees symmetrizable hyperbolicity by construction.
Adaptive change of basis in entropy-based moment closures for linear kinetic equations.Journal of Computational Physics, 258:489–508, 2014
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions
A block-diagonal symmetrizer and algebraic conditions on closure blocks enable a data-learnable parametrization of ML moment closures for 2D RTE that guarantees symmetrizable hyperbolicity by construction.