arXiv (All)
open access
$H^2(Ω)$ A Priori Error Bounds for Boundary-Adapted PINNs Solving Elliptic Dirichlet Problems
We study the numerical resolution of uniformly elliptic Dirichlet boundary value problems (BVPs) by minimization of the PDE residual over a hypothesis space of Neural Networks made to satisfy exactly the boundary conditions through multiplication by a user-defined \emph{distance-to-boundary approxim
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Sharp Global Well-Posedness for the Logarithmically Regularized 2D Euler Equations#1354990
- Residual-Based Adaptive Refinement in Hybrid Variational-Collocation PINNs for Singular Elliptic PDEs with Mixed Dirichlet-Neumann Boundary Conditions#1401625
- Asymptotics of Laplace and Stokes solutions in the gap between a sharp tip and a smooth boundary#1123742
- Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems#785072
- Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems#783482
Record · ID 1403181
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive