新方法使用连续神经网络表示随机混合系统

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种使用连续神经网络表示随机混合系统（SHS）的新颖方法。该方法通过编码辅助变量将SHS复杂的重置动力学转化为确定性动力学，使其能够嵌入到更高维的欧几里得空间中。由此产生的模型，即单一的潜在SDE，可以在不需要显式重置项、模式标签或基于事件的模拟的情况下，恢复SHS的概率演化。 AI

影响这项研究可能导致在生物化学过程等领域对复杂系统进行更有效的建模。

排序理由该集群包含一篇详细介绍随机混合系统表示新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Sangli Teng, Hang Liu, Koushil Sreenath · 2026-10-01 04:00

学习随机混合系统的连续神经表示

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 …