Machine learning has become a premier tool in physics and other fields of science. It has been shown that the quantum mechanical scattering problem cannot only be solved with such techniques, but it was argued that the underlying neural network develops the Born series for shallow potentials. However, classical machine learning algorithms fail in the unitary limit of an infinite scattering length. The unitary limit plays an important role in our understanding of bound strongly interacting fermionic systems and can be realized in cold atom experiments. Here, we develop a formalism that explains the unitary limit in terms of what we define as unitary limit surfaces. This not only allows to investigate the unitary limit geometrically in potential space, but also provides a numerically simple approach towards unnaturally large scattering lengths with standard multilayer perceptrons. Its scope is therefore not limited to applications in nuclear and atomic physics, but includes all systems that exhibit an unnaturally large scale.

机器学习已成为物理学和其他科学领域的首要工具。已经表明，量子力学散射问题不能仅用此类技术解决，但有人认为底层神经网络为浅层电位开发了波恩级数。然而，经典机器学习算法在无限散射长度的单一限制中失败。幺正极限在我们理解束缚强相互作用费米子系统中起着重要作用，并且可以在冷原子实验中实现。在这里，我们开发了一种形式主义，根据我们定义的幺正极限曲面来解释幺正极限。这不仅允许在潜在空间中几何地研究幺正极限，但也提供了一种数值简单的方法来处理标准多层感知器的不自然的大散射长度。因此，它的范围不仅限于核物理和原子物理学中的应用，还包括表现出非自然大规模的所有系统。