论文标题
使用ISING蠕虫模拟上的机器学习减少错误
Error reduction using machine learning on Ising worm simulation
论文作者
论文摘要
我们开发了一种使用机器学习技术来改善更高矩的统计错误的方法。我们在这里介绍了ISING模型与外部场的双重表示的结果，该模型是通过高温膨胀得出的，并由蠕虫算法模拟的结果。我们比较了两种测量相同的可观测值的方法，没有机器学习和机器学习：可以通过使用决策树方法来训练较高的矩与从集成的2点函数获得的较高的矩与第二个时刻之间的相关性来提高磁化和易感性。将这些结果少量与分析预测进行比较。
We develop a method to improve on the statistical errors for higher moments using machine learning techniques. We present here results for the dual representation of the Ising model with an external field, derived via the high temperature expansion and simulated by the worm algorithm. We compare two ways of measuring the same set of observables, without and with machine learning: moments of the magnetization and the susceptibility can be improved by using the decision tree method to train the correlations between the higher moments and the second moment obtained from an integrated 2-point function. Those results are compared in small volumes to analytic predictions.
