Generative models, such as the method of normalizing flows, have been suggested as alternatives to the standard algorithms for generating lattice gauge field configurations. Studies with the method of normalizing flows demonstrate the proof of principle for simple models in two dimensions. However, further studies indicate that the training cost can be, in general, very high for large lattices. The poor scaling traits of current models indicate that moderate-size networks cannot efficiently handle the inherently multi-scale aspects of the problem, especially around critical points. We explore current models with limited acceptance rates for large lattices and examine new architectures inspired by effective field theories to improve scaling traits. We also discuss alternative ways of handling poor acceptance rates for large lattices.
翻译:已经建议采用生成模型,例如正常流动的方法,作为产生拉蒂测量仪实地配置的标准算法的替代方法。关于正常流动方法的研究证明简单模型的原则有两个方面。然而,进一步的研究显示,对于大型顶层来说,培训成本一般可能很高。当前模型的缩放特征不高,表明中等规模的网络无法有效处理问题固有的多尺度方面,特别是关键点周围。我们探索目前大型顶层接收率有限的模型,并研究受有效实地理论启发的新结构,以改进缩放特性。我们还讨论了处理大型顶层接受率低的替代方法。