arXiv (All)
open access
Mapped Multi-Patch Spectral Extreme Learning Machine for Partial Differential Equations
We study a fixed-feature solver, referred to as Spectral-ELM, in which Chebyshev--Gauss--Lobatto (CGL) differentiation matrices are applied to the nodal values of an extreme learning machine (ELM) trial function. Curved domains are treated using a mapped multi-patch formulation. Each curved quadrila
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
- Cross-Condition Fault Diagnosis of Crane Slewing Bearings Based on a Lightweight Domain-Adaptive Graph Convolutional Network#683423
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.#823514
- String kernel representations in elastostatics#1014720
- Certified geometric robustness -- Super-DeepG#138948
- Extreme Learning Machine for embedded neural networks. (Extreme Learning Machine pour réseau de neurones embarqué).#489528
Record · ID 1115865
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