Short autocorrelation times are essential for a reliable error assessment in Monte Carlo simulations of lattice systems. In many interesting scenarios, the decay of autocorrelations in the Markov chain is prohibitively slow. Generative samplers can provide statistically independent field configurations, thereby potentially ameliorating these issues. In this work, the applicability of neural samplers to this problem is investigated. Specifically, we work with a generative adversarial network (GAN). We propose to address difficulties regarding its statistical exactness through the implementation of an overrelaxation step, by searching the latent space of the trained generator network. This procedure can be incorporated into a standard Monte Carlo algorithm, which then permits a sensible assessment of ergodicity and balance based on consistency checks. Numerical results for real, scalar φ 4 -theory in two dimensions are presented. We achieve a significant reduction...

中文翻译：

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使用生成对抗网络减少晶格模拟中的自相关时间

短自相关时间对于晶格系统的蒙特卡洛模拟中的可靠误差评估至关重要。在许多有趣的场景中，马尔可夫链中自相关的衰减非常缓慢。生成式采样器可以提供统计上独立的字段配置，从而有可能改善这些问题。在这项工作中，研究了神经采样器对这个问题的适用性。具体来说，我们与生成对抗网络（GAN）合作。我们建议通过搜索经过训练的发电机网络的潜在空间，通过实施超松弛步骤来解决有关统计准确性的难题。该过程可以合并到标准的Monte Carlo算法中，然后，根据一致性检查，可以合理地评估遍历性和平衡性。给出了二维二维实量标量φ4理论的数值结果。我们大大减少了...