Modelling dynamical systems is an integral component for understanding the natural world. To this end, neural networks are becoming an increasingly popular candidate owing to their ability to learn complex functions from large amounts of data. Despite this recent progress, there has not been an adequate discussion on the architectural regularization that neural networks offer when learning such systems, hindering their efficient usage. In this paper, we initiate a discussion in this direction using coordinate networks as a test bed. We interpret dynamical systems and coordinate networks from a signal processing lens, and show that simple coordinate networks with few layers can be used to solve multiple problems in modelling dynamical systems, without any explicit regularizers.

翻译：模拟动态系统是了解自然界的一个组成部分。 为此,神经网络由于能够从大量数据中学习复杂功能,正日益成为受欢迎的候选人。尽管最近取得了这一进展,但还没有就神经网络在学习这些系统时提供的建筑规范进行充分讨论,从而阻碍了这些系统的有效使用。在本文中,我们利用协调网络作为测试台,以此为方向展开讨论。我们从信号处理镜头中解释动态系统和协调网络,并表明可以使用几层的简单协调网络来解决模拟动态系统中的多种问题,而没有明确的规范。</s>