标题： 机器学习拓扑缺陷形成 显示英文标题

标题： Machine learning topological defect formation

摘要： 根据基布尔-祖雷克机制（KZM），在二阶相变过程中产生的拓扑缺陷的密度由冻结时间处的相关长度决定。 这表明，在这种转变中，拓扑缺陷的最终配置主要在冲激阶段建立，即在临界点被穿越后不久。 受此启发，我们推测机器学习（ML）可以根据临界点附近短时间内序参量的时间演化，预测拓扑缺陷的最终配置，远早于序参量进入由于自发对称性破缺而产生的新极小值。 此外，我们证明了ML的可预测性也遵循KZM所规定的幂律标度。 我们使用循环神经网络来证明这些结论。 显示英文摘要

摘要： According to the Kibble-Zurek mechanism (KZM), the density of topological defects created during a second-order phase transition is determined by the correlation length at the freeze-out time. This suggests that the final configuration of topological defects in such a transition is largely established during the impulse regime, soon after the critical point is traversed. Motivated by this, we conjecture that machine learning (ML) can predict the final configuration of topological defects based on the time evolution of the order parameter over a short interval in the vicinity of the critical point, well before the order parameter settles into the emerging new minima resulting from spontaneous symmetry breaking. Furthermore, we show that the predictability of ML also follows the power law scaling dictated by KZM. We demonstrate these using a Recurrent Neural Network.