径向薛定谔方程的机器学习S波散射相移
Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schrödinger Equation
摘要：
我们提出了一个基于卷积神经网络的概念机器学习模型的证明，该模型能够在固定的碰撞能量下产生由不同的三维球对称势引起的精确散射s波相移，从而绕过径向薛定谔方程。在我们的工作中，我们讨论了如何哈密顿可以作为一个指导原则，在建设一个物理动机的描述。即使在数据集中存在束缚态的情况下，我们的模型也表现出良好的性能，该模型相应地通过每个散射势在哈密顿量上进行训练，证明了这种原理证明的可行性。
英文摘要：
We present a proof of concept machine learning model resting on a convolutional neural network capable to yield accurate scattering s-wave phase shifts caused by different three-dimensional spherically symmetric potentials at fixed collision energy thereby bypassing the radial Schrödinger equation. In out work, we discuss how the Hamiltonian can serve as a guiding principle in the construction of a physically-motivated descriptor. The good performance, even in presence of bound states in the data sets, exhibited by our model that accordingly is trained on the Hamiltonian through each scattering potential, demonstrates the feasibility of this proof of principle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
