论文标题
在平衡之前从蒙特卡洛样品中快速检测到相变
Rapid detection of phase transitions from Monte Carlo samples before equilibrium
论文作者
论文摘要
我们发现双向LSTM和变压器可以通过在平衡之前通过学习蒙特卡洛原始数据中的学习特征来确定凝结物质模型的不同阶段，并确定相变点。与常规的蒙特卡洛模拟相比，我们的方法可以大大减少探测相变所需的时间和计算资源。我们还提供了证据表明该方法是鲁棒的，并且深度学习模型的性能对输入数据的类型不敏感（我们测试了经典模型的自旋配置和量子模型的绿色功能），并且在检测Kosterlitz-theless the-the-the-the-the-thepereprientions方面也表现良好。
We found that Bidirectional LSTM and Transformer can classify different phases of condensed matter models and determine the phase transition points by learning features in the Monte Carlo raw data before equilibrium. Our method can significantly reduce the time and computational resources required for probing phase transitions as compared to the conventional Monte Carlo simulation. We also provide evidence that the method is robust and the performance of the deep learning model is insensitive to the type of input data (we tested spin configurations of classical models and green functions of a quantum model), and it also performs well in detecting Kosterlitz-Thouless phase transitions.
