We present our new AIverse project in which several algorithm of artificial intelligence (AI) are used to simulate, analyze, and describe the physical properties of galaxies and star clusters. The three main purposes of the project are to (i) classify the formationand evolution processes of galaxies and star clusters, (ii) perform computer simulations in an automatic way, and (iii) convert the animation produced by the simulation into sentences using AI. Here we focus exclusively on the first component of the project as follows. We use convolutional neural networks (CNNs) to classify the formation and evolution processes of galaxies based on the two-dimensional (2D) images of galactic properties such as mass densities. This new classification method is two-stage as follows. First a large number of the synthesized 2D images of galactic properties from computer simulations are used to train a CNN for the classification. Once the CNN comes to have a very high accuracy, the CNN is then used to classify the real observational data. We discuss the effectiveness of the new classification method using the results of computer simulations on one of key formation processes of galaxies. We also discuss the number of images ($N_{\mathrm{i}}$) required to generate from computer simulations by investigating the models with different $N_{\mathrm{i}}$ and other parameters. We briefly outline the other two components of the project and discuss their purposes.

我们提出了新的AIverse项目，其中使用了几种人工智能（AI）算法来模拟，分析和描述星系和恒星团的物理特性。该项目的三个主要目的是（i）对星系和恒星团的形成和演化过程进行分类，（ii）以自动方式执行计算机模拟，以及（iii）使用AI将模拟产生的动画转换为句子。在这里，我们专门关注项目的第一部分，如下所示。我们使用卷积神经网络（CNN）根据银河性质（例如质量密度）的二维（2D）图像对星系的形成和演化过程进行分类。这种新的分类方法分为以下两个阶段。首先，来自计算机模拟的大量银河性质合成2D图像用于训练CNN进行分类。一旦CNN的准确性很高，就可以使用CNN对真实的观测数据进行分类。我们使用计算机模拟星系关键形成过程之一的结果来讨论新分类方法的有效性。我们还将讨论图片数量（${ñ}_{\mathrm{一世}}$）需要通过研究具有不同模型的计算机模拟来生成 ${ñ}_{\mathrm{一世}}$和其他参数。我们简要概述了该项目的其他两个组成部分，并讨论了它们的目的。