arXiv Artificial Intelligence

Learning Continuous Neural Representation of Stochastic Hybrid Systems

2 hours ago 01.10.2026 07:00 👁 2 views
Learning Continuous Neural Representation of Stochastic Hybrid Systems

Quick summary

arXiv:2609.38893v1 Announce Type: cross Abstract: 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 corresponding to the SDE and an integral term arising from the reset kernel. This work shows that such an SHS can be approximated by an SDE in a higher-dimensional latent space where the sample paths are continuous. The key to this result is to encode

Key takeaways

  • arXiv:2609.38893v1 Announce Type: cross Abstract: 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 corresponding to the SDE and an integral term arising from the reset kernel.
  • This work shows that such an SHS can be approximated by an SDE in a higher-dimensional latent space where the sample paths are continuous.

Why it matters

The importance of “Learning Continuous Neural Representation of Stochastic Hybrid Systems” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗