论文标题
多目标物理引导的复发神经网络，用于识别非自治动力学系统
Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
论文作者
论文摘要
尽管建模工作和模型准确性之间的权衡仍然是系统识别的主要问题，但诉诸于数据驱动的方法通常会导致完全无视物理上的合理性。为了解决这个问题，我们提出了一种物理引导的混合方法，用于对正在控制的非自主系统进行建模。从传统的基于物理的模型开始，这是通过复发性神经网络扩展的，并使用复杂的多目标策略进行了训练，从而产生了物理上合理的模型。尽管纯粹的数据驱动方法无法产生令人满意的结果，但实际数据进行的实验揭示了与基于物理学的模型相比，我们的方法的实质性准确性提高了。
While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model.
