MgNet learns the RTE solution operator via a multigrid-inspired architecture with neural sub-operators and adaptive angular filtering, delivering at least 10x speedup as a preconditioner with generalization to new parameters.
Asymptotic solutions of numerical transport problems in optically thick, diffusive regimes.Journal of Computational Physics, 69(2):283–324
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.NA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
An adaptive tailored finite point scheme combined with the recursive skeleton method produces a reusable explicit multilevel decomposition of the inverse operator for sequences of steady-state RTEs arising from implicit time discretization.
citing papers explorer
-
A Filtered MgNet Solver For Radiative Transfer Equations
MgNet learns the RTE solution operator via a multigrid-inspired architecture with neural sub-operators and adaptive angular filtering, delivering at least 10x speedup as a preconditioner with generalization to new parameters.
-
Fast Algorithm For Solving Time-dependent Multiscale radiative transport Equation
An adaptive tailored finite point scheme combined with the recursive skeleton method produces a reusable explicit multilevel decomposition of the inverse operator for sequences of steady-state RTEs arising from implicit time discretization.