arXiv (All)
open access
Error Analysis for Solving Elliptic Interface Problems with Randomized Neural Networks
This paper presents a unified error analysis of randomized neural networks (RaNNs) for elliptic interface problems. We first derive an integral representation of Barron functions using the standard $\tanh$ activation, which yields a $C^2$-approximation bound valid for any hidden-parameter distributi
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
- Locally computable error estimators for conforming approximations of interface problems cannot be robust#1064361
- Algebraic convergence analysis for the Interface Control Domain Decomposition (ICDD) method#1123649
- Residual-Guided Randomized Neural Networks#792288
- Residual-Guided Randomized Neural Networks#788288
- Official Review · ICLR 2026#787219
Record · ID 1062424
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