arXiv (All)
open access
Solution-space heterogeneity shapes federated learning dynamics across partial differential equations
Federated scientific machine learning enables institutions to train neural surrogates without centralizing local physical data, yet studies of partial differential equations (PDEs) lack a transferable definition of non-independent and identically distributed data. Existing protocols partition coordi
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
- Solution-space heterogeneity shapes federated learning dynamics across partial differential equations#660780
- Mechanistic PDE networks for discovery of governing equations#1079711
- Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics#984708
- Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees#820393
- Physics and Data Driven Transformer-Mamba Framework for Flow Field#1077016
Record · ID 971970
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