Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints
This paper proposes a hard-constrained neural Riemann solver (HCNRS) that enforces five physical constraints—positivity, consistency, mirror symmetry, Galilean invariance, and scaling invariance—to accurately approximate exact Riemann solvers for fluid dynamics while overcoming the conservation errors and symmetry breaking issues found in unconstrained neural approaches.