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.
arXiv preprint arXiv:2510.05568 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A multifidelity cokriging kernel-learning approach is proposed to construct high-fidelity kernels and means for Gaussian process solutions of nonlinear PDEs, demonstrated on Burgers' equation.
citing papers explorer
-
Multifidelity Gaussian process regression for solving nonlinear partial differential equations
A multifidelity cokriging kernel-learning approach is proposed to construct high-fidelity kernels and means for Gaussian process solutions of nonlinear PDEs, demonstrated on Burgers' equation.