The authors combine H(div)-L2 subspaces from Raviart-Thomas and dgP0 elements with a transformer and GP regression on fluxes to create real-time structure-preserving surrogates with closed-form posterior uncertainty for Dirichlet-to-Neumann maps.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification
The authors combine H(div)-L2 subspaces from Raviart-Thomas and dgP0 elements with a transformer and GP regression on fluxes to create real-time structure-preserving surrogates with closed-form posterior uncertainty for Dirichlet-to-Neumann maps.