arXiv (All)
open access
A Locally Conservative Enriched Linearized Neural Network Approximation to Elliptic PDEs
This paper presents a locally conservative Enriched Linearized Neural Network (ENN) method for the Darcy flow model. A linearized shallow ReLU$^k$ network, whose hidden-layer parameters are fixed on a quasi-uniform set of the sphere, carries the approximation, and piecewise constant functions on an
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
- Unifying Graph Neural Networks Through a Common Layer Equation#620512
- Unifying Graph Neural Networks Through a Common Layer Equation#618833
- Hard-Trace First-Order Residual Learning for Coupled Stokes-Brinkman-Darcy Flow#1131994
- Global Exponential Convergence of Two-Layer Linear Network Training#1233395
- Adaptive back-propagation in on-line learning of multilayer networks#978333
Record · ID 1200175
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