arXiv (All)
open access
Bayesian Safety Guarantees for Port-Hamiltonian Systems with Learned Energy Functions
Control barrier functions for port-Hamiltonian systems inherit model uncertainty when the Hamiltonian is learned from data. We show how to propagate this uncertainty into a safety filter with independently tunable credibility budgets. To propagate this uncertainty, we employ a two-stage Bayesian app
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
- Robust design under uncertainty in quantum error mitigation#183138
- Robust design under uncertainty in quantum error mitigation#183088
- Robust design under uncertainty in quantum error mitigation#183038
- Robust design under uncertainty in quantum error mitigation#182988
- Mitigating False Positives in Static Memory Safety Analysis of Rust Programs via Reinforcement Learning#155356
Record · ID 1036425
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