- Mühendislik · arxiv/cs.LG · 8 dk
Learning turbulence closures via nudging sidesteps solver backprop
A data-assimilation-inspired approach trains neural network turbulence models on DNS data without embedding them in solvers, reducing computational cost and improving stability.
28 Nisan 2026 Oku → → - Mühendislik · arxiv/cs.LG · 4 dk
Hybrid PINNs: Finite-Difference Regularization for Physics Solvers
Adding weak finite-difference gradient penalties to physics-informed neural networks improves boundary accuracy without replacing automatic-differentiation residuals.
17 Nisan 2026 Oku → →