arXiv (All)
open access
Green-Routed Neural Operators:\\Physics Determines Where the Network Reads
We identify a mismatch between the physical role of transport fields in many PDEs and their usual role in neural operators: PDEs use them to select read coordinates, whereas neural operators typically treat them only as input values. We address this mismatch with the Green-Routed Neural Operator (GR
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
- Physics-Informed Non-Dimensionalisation for Neural Solvers#684012
- Shared Physics Responses Recover Hidden Rankings in Neural Operator Libraries#659013
- Shared Physics Responses Recover Hidden Rankings in Neural Operator Libraries#656877
- Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures#927698
- Advectra: Asymmetric Latent Transport for Non-Stationary Physics#1201111
Record · ID 1201350
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