The interest in leveraging physics-based inductive bias in deep learning has resulted in recent developments of hybrid deep generative models (hybrid-DGMs) that integrates known physics-based mathematical expressions in neural generative models. The identification of these hybrid-DGMs often involves the inference of the parameters of the physics-based component along with its neural component. The identfiability of these hybrid-DGM however has not yet been theoretically probed or established. While the (un)identfiability of DGMs has been investigated, existing solutions often do not apply here as they require observed auxiliary labels about the underlying hybrid models. This paper provides the first theoretical probe of the identfiability of hybrid-DGMs, and present meta-learning as a novel solution to construct identifiable hybrid-DGMs. On synthetic and real-data benchmarks, we further provide strong evidence for the un-identifiability of existing hybrid-DGMs using unconditional priors, and strong identifiability results of meta-hybrid-DGMs.