Modern applications increasingly require unsupervised learning of latent
dynamics from high-dimensional time-series. This presents a significant
challenge of identifiability: many abstract latent representations may
reconstruct observations, yet do they guarantee an adequate identification of
the governing dynamics? This paper investigates this challenge from two angles:
the use of physics inductive bias specific to the data being modeled, and a
learn-to-identify strategy that separates forecasting objectives from the data
used for the identification. We combine these two strategies in a novel
framework for unsupervised meta-learning of hybrid latent dynamics (Meta-HyLaD)
with: 1) a latent dynamic function that hybridize known mathematical
expressions of prior physics with neural functions describing its unknown
errors, and 2) a meta-learning formulation to learn to separately identify both
components of the hybrid dynamics. Through extensive experiments on five
physics and one biomedical systems, we provide strong evidence for the benefits
of Meta-HyLaD to integrate rich prior knowledge while identifying their gap to
observed data.

从数据库中无监督地学习高维时间序列的潜在动力学是一个挑战，该论文从物理归纳偏差和学习 - 识别策略的角度研究了这个问题，并提出一种新颖框架 Meta-HyLaD，用于无监督元学习混合潜在动力学，既包括已知的数学表达式又包括描述未知误差的神经函数，并通过对五个物理系统和一个生物医学系统的广泛实验证据来说明 Meta-HyLaD 整合丰富的先前知识优势并识别其与观测数据之间的差距。