论文标题
可解释的机器学习模型：基于物理的观点
Interpretable machine learning models: a physics-based view
论文作者
论文摘要
了解物理系统的变化并促进决策，解释如何做出模型预测至关重要。我们使用基于模型的可解释性，其中通过组成基本结构来构建物理系统的模型，这些基本结构在本地解释了如何交换和转换能源。我们使用汉密尔顿港（P-H）形式主义来描述包含物理系统行为中常见的物理解释过程的基本结构。我们描述了如何从P-H结构中构建模型以及如何训练它们。此外，我们还展示了如何施加物理特性，例如耗散性，以确保训练过程的数值稳定性。我们举例说明了如何构建和训练模型来描述两个物理系统的行为：倒的摆和群体动力学。
To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial. We use model-based interpretability, where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed. We use the port Hamiltonian (p-H) formalism to describe the basic constructs that contain physically interpretable processes commonly found in the behavior of physical systems. We describe how we can build models out of the p-H constructs and how we can train them. In addition we show how we can impose physical properties such as dissipativity that ensure numerical stability of the training process. We give examples on how to build and train models for describing the behavior of two physical systems: the inverted pendulum and swarm dynamics.
