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Learning Dynamical Systems across Environments

Learning the behavior of natural phenomena automatically from the data has gained much traction these last years. However, in most real world scenarios, the environment in which the data samples are acquired is varying and may not be the same for each data sample. This is due to different circumstances e.g. acquisition in different spatial locations, or simply experimental settings which slightly differ. This severely hinders the training process, and makes the standard learning framework inapplicable. In this work, we propose a novel framework for modeling physical systems in this context, where we are able to leverage the data across different environments in order to learn the underlying dynamical systems, ensuring generalization without compromising the model’s expressiveness and predictive performance. We instantiate our framework on two different families of dynamical systems, proving that our approach yields superior results over the classical learning approach as well as against competitive baselines. Finally, we also show that we are also able to accelerate and improve the learning for environments that have never been seen before.