Recently, there has been significant research on the connection between physics theory and machine learning. As a way to approach physics theory from machine learning, there has been a study on the universe that learns its own laws based on the fact that quantum field theory and learning system are expressed as a matrix model in much the same way. In the opposite position, certain familiar symmetries have been required for conventional convolutional neural netwoks(CNNs) for performance improvement, and as a result, CNNs have come to be expressed in a covariant form that physics theory must satisfy. These positive signals can be a driving force for studying physics theory using machine learning, but in reality there are several difficulties in implementing a working system. First of all, just because the convoultion can be expressed in covariant form, it is not obvious to implement the algorithm corresponding to that expression. At the beginning of this paper, we show that it is possible to reach covariant CNNs through the proposed method without implementing the specific algorithm. However, the more serious problem is that there is still insufficient discussion on how to collect a well-defined data set corresponding to the law to be learned. Therefore, in the current situation, it would be best to simplify the problem to satisfy some physical requirements and then see if it is possible to learn with the corresponding neural-networks architecture. In this point of view, we demonstrate to learning process of cellular automata(CA) that could satisfy locality,time-reversibility through CNNs. With simple rules that satisfy the above two conditions and an arbitrary dataset that satisfies those rules, CNNs architecture that can learn rules were proposed and it was confirmed that accurate inference, that is, an approximation of the equation was made for simple examples.
翻译:最近,对物理学理论和机器学习之间的联系进行了大量研究。作为从机器学习中处理物理学理论的一种方法,对宇宙进行了一项研究,根据量子场理论和学习系统以基本模型模式以非常相同的方式表达为矩阵模型这一事实,对宇宙进行了自己的法律学习。相反的立场是,对于常规神经神经神经网络(CNNs),需要某些熟悉的对称来改进性能,结果有线电视新闻网以物理理论必须满足的易变形式表达。这些积极信号可以成为利用机器学习研究物理理论的动力,但在现实中,实施工作系统存在一些困难。首先,由于可以可变式形式表达的调和系统,因此执行与该表达法相对应的算法并不明显。在本文的开头,我们表明,通过拟议的方法可以达到易变的CNN,而没有采用具体的算法。然而,更严重的问题是,对于如何用机器学习精确的数据集成一个精确的物理规则,如果通过直径直线路路路规则,则可以学习一个最精确的直径直径。因此,在实际结构中可以学习一个最精确的直径直径直径直径直径直线路路路路。因此,因此,在学习中可以学习一个直径径径直路路路路路路。因此,这样学习一个可能的直路路路路路。