Learning ground states of spin-orbit-coupled Bose-Einstein condensates by a theory-guided neural network

Abstract

The ground state of spin-orbit-coupled (SOC) Bose-Einstein condensates (BECs) is an intriguing subject that has not yet been fully explored. In particular, how to quickly obtain ground states for discovering their new structures is currently under active debate. In this work, we construct a theory-guided neural network (TgNN) to explore the ground states of one-dimensional BECs with and without SOC. We find that such method is markedly superior to the ordinary deep neural network due to theory guidance of the underlying problems. The former can replace the traditional method to directly give any ground state within a valid parameter plane using only imaginary-time evolution data of nine solutions as the training data, without the tedious step-by-step iterative calculation process. Furthermore, we show that the method of TgNN exhibits higher reliability and a better ability to generalize beyond regimes covered with the training data. This method provides a promising technique for discovering novel structures of ground states in parameterized spatiotemporal systems.