论文标题
无监督的神经网络，用于量子特征值问题
Unsupervised Neural Networks for Quantum Eigenvalue Problems
论文作者
论文摘要
特征值问题对于科学和工程的几个领域至关重要。我们提出了一个新型的无监督神经网络，用于发现与差异特征值问题的特征和特征值，这些问题与解决边界条件的解决方案相同。嵌入扫描机制，允许该方法找到任意数量的解决方案。网络优化是无数据的，仅取决于预测。无监督的方法用于解决量子无限井和量子振荡器特征值问题。
Eigenvalue problems are critical to several fields of science and engineering. We present a novel unsupervised neural network for discovering eigenfunctions and eigenvalues for differential eigenvalue problems with solutions that identically satisfy the boundary conditions. A scanning mechanism is embedded allowing the method to find an arbitrary number of solutions. The network optimization is data-free and depends solely on the predictions. The unsupervised method is used to solve the quantum infinite well and quantum oscillator eigenvalue problems.
