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.

翻译：流形可视化技术通常用于物理科学中高维数据集的可视化。本文中，我们将一种新引入的流形可视化方法——Slise 应用于物理和化学数据集。Slisemap 将流形可视化与可解释人工智能相结合。可解释人工智能用于探究黑箱机器学习模型及复杂模拟器的决策过程。通过 Slisemap，我们能够找到一个嵌入表示，使得具有相似局部解释的数据项被分组在一起。因此，Slisemap 为我们提供了黑箱模型不同行为的概览。这使得 Slisemap 成为一种有监督的流形可视化方法，其中嵌入中的模式反映了目标属性。本文展示了如何对物理数据使用和评估 Slisemap，并说明 Slisemap 有助于在这些数据集上训练的分类和回归模型中寻找有意义的信息。