Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper we apply a recently introduced manifold visualisation method, called Slise, on datasets from physics and chemistry. Slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence is used to investigate the decision processes of black box machine learning models and complex simulators. With Slisemap we find an embedding such that data items with similar local explanations are grouped together. Hence, Slisemap gives us an overview of the different behaviours of a black box model. This makes Slisemap into a supervised manifold visualisation method, where the patterns in the embedding reflect a target property. In this paper we show how Slisemap can be used and evaluated on physical data and that Slisemap is helpful in finding meaningful information on classification and regression models trained on these datasets.

使用Slisemap方法对物理和化学数据集进行流形可视化，并结合可解释人工智能探索黑盒机器学习模型和复杂模拟器的决策过程，从而获得具有相似局部解释的数据项聚集的嵌入式视图，提供黑盒模型不同行为的总体概览，使Slisemap成为一种有监督的流形可视化方法，嵌入式模式反映目标属性，并展示了如何在物理数据上使用和评估Slisemap，以及Slisemap在这些数据集上训练的分类和回归模型中发现有意义的信息。

利用Slisemap解释物理数据