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.
Machine learning surrogate models for Landau fluid closure.Physics of Plasmas, 27(4):042502, 2020
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.