arXiv CS
open access
Singular parameters and missing limits in neural PDE solvers
Neural solvers for partial differential equations (PDEs) can approach an accurate solution while their parameters grow without bound. In such cases, the limiting solution may have no finite representation in the chosen model, leaving the best loss unattained. Our analysis connects missing limits in
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
- Singular parameters and missing limits in neural PDE solvers#1215856
- Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers#224524
- Neural network methods for PDEs and Perron-Frobenius operator problems: Neumann series, invariant densities, and test-space adaptivity#672803
- CEENs: Causality-enforced evolutional networks for solving time-dependent partial differential equations#1200056
- ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs#321851
Record · ID 1194430
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