arXiv (All)
open access
Learning Continuous Neural Representation of Stochastic Hybrid Systems
A stochastic hybrid system (SHS) is governed by a stochastic differential equation (SDE) describing the continuous dynamics and a Markov reset kernel triggered on the guard surface. Its probability evolution can be described by a hybrid Fokker-Planck (HFP) equation with a partial differential term c
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
- Salted Fisher Information for Hybrid Systems#1062874
- Geometric Hybrid Dynamical Systems: Part I - Modeling and Stability#1013723
- Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson’s disease#425902
- Strict SDE Comparison for Cusp Coefficients and Counterexamples#1035638
- Modélisation bayésienne du système lire-écrire#171512
Record · ID 1143494
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