Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts. We propose a new framework for this key problem, context-informed dynamics adaptation (CoDA), which takes into account the distributional shift across systems for fast and efficient adaptation to new dynamics. CoDA leverages multiple environments, each associated to a different dynamic, and learns to condition the dynamics model on contextual parameters, specific to each environment. The conditioning is performed via a hypernetwork, learned jointly with a context vector from observed data. The proposed formulation constrains the search hypothesis space to foster fast adaptation and better generalization across environments. It extends the expressivity of existing methods. We theoretically motivate our approach and show state-ofthe-art generalization results on a set of nonlinear dynamics, representative of a variety of application domains. We also show, on these systems, that new system parameters can be inferred from context vectors with minimal supervision.

提出了一种新的框架CoDA，旨在解决数据驱动模拟物理系统无法泛化到共享相同一般动态的新系统的问题，该框架结合了上下文信息的动态调整和超网络，实现了快速的适应和更好的泛化性能。

通过上下文感知的动态模型实现对新物理系统的泛化