Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this problem is to train recurrent models. However, predictions with these models are often not physically meaningful. Further, they suffer from deteriorated behavior over time due to accumulating errors. Often, simulators building on first principles are available being physically meaningful by design. However, modeling simplifications typically cause inaccuracies in these models. Consequently, hybrid modeling is an emerging trend that aims to combine the best of both worlds. In this paper, we propose a new approach to hybrid modeling, where we inform the latent states of a learned model via a black-box simulator. This allows to control the predictions via the simulator preventing them from accumulating errors. This is especially challenging since, in contrast to previous approaches, access to the simulator's latent states is not available. We tackle the task by leveraging observers, a well-known concept from control theory, inferring unknown latent states from observations and dynamics over time. In our learning-based setting, we jointly learn the dynamics and an observer that infers the latent states via the simulator. Thus, the simulator constantly corrects the latent states, compensating for modeling mismatch caused by learning. To maintain flexibility, we train an RNN-based residuum for the latent states that cannot be informed by the simulator.

翻译：动力学模型学习涉及从测量数据中推断未知动力学，并预测系统未来行为的问题。典型方法是训练循环神经网络模型，但此类模型的预测结果往往缺乏物理意义，且随时间推移因误差累积导致性能退化。尽管基于第一性原理构建的模拟器在物理层面具有合理设计，但其建模简化通常带来不准确性。因此，混合建模作为结合两者优势的新兴趋势应运而生。本文提出一种新型混合建模方法，通过黑箱模拟器对学习模型的潜在状态进行信息注入，从而借助模拟器控制预测过程防止误差累积。与现有方法不同，本方案面临的核心挑战在于无法获取模拟器的潜在状态。我们通过引入控制理论中的观测器概念来解决该问题——该观测器可从观测数据与时变动力学中推断未知潜在状态。在基于学习的框架中，我们联合学习动力学模型及通过模拟器推断潜在状态的观测器。由此，模拟器持续修正学习模型的潜在状态，补偿因学习过程产生的建模偏差。为保持灵活性，我们还训练了基于循环神经网络的残差模块，用于处理无法从模拟器获取信息的潜在状态分量。