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.
Evaluation of quadrature schemes for the discrete ordinates method.Journal of Quantitative Spectroscopy and Radiative Transfer, 84(4):423–435, 2004
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.