提出双投影方法，同时重建系统状态与噪声，提升动态系统建模精度。
Double projection for reconstructing dynamical systems: between stochastic and deterministic regimes
- 通过双投影同时估计状态轨迹和噪声序列
- 在6个基准任务中实现低维状态空间下的多步预测
- 适合需要融合随机性与确定性建模的科研场景
从观测数据中学习动态系统的随机模型在多个科学领域具有重要意义。本文提出一种新的方法，属于动态变分自编码器家族。所提双投影方法可从数据中同时估计系统状态轨迹与噪声时间序列。该方法自然支持多步系统演化，并能学习低维状态空间的模型。我们在六个基准问题上评估了该方法的性能，涵盖模拟与实验数据。进一步分析了多步方案中教师强制间隔对内部动态特性的影响，并与等效架构的确定性模型行为进行比较。
原文摘要 · Abstract (English)
Learning stochastic models of dynamical systems from observed data is of interest in many scientific fields. Here, we propose a new method for this task within the family of dynamical variational autoencoders. The proposed double projection method estimates both the system state trajectories and the noise time series from data. This approach naturally allows us to perform multi-step system evolution and to learn models with a comparatively low-dimensional state space. We evaluate the performance of the method on six benchmark problems, including both simulated and experimental data. We further illustrate the effects of the teacher forcing interval of the multi-step scheme on the nature of the internal dynamics and compare the resulting behavior to that of deterministic models of equivalent architecture.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。