arXiv (All)
open access
Extreme Learning Machine-based Barrier Function Synthesis
Deep learning methods like neural networks have greatly simplified the computation of safety certificates for complex nonlinear systems with unknown dynamics. However, due to the data-driven nature of these certificates and the complex architecture of neural networks, computation time as well as rob
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
- LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics#1013250
- Optimal scenario design for climate emulation#1141574
- Improving Certified Robustness via Adversarial Distillation#324909
- Online construction of reliable hybrid-AI models from deviation data and physics-based information#643177
- Explaining El Niño Forecasts with the Average Gradient Outer Product#1156030
Record · ID 1144188
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