论文标题
晶格场理论采样的生成模型
Generative models for sampling of lattice field theories
论文作者
论文摘要
我们探索了基于对抗性非线性独立组件估计蒙特卡洛的自学马尔可夫链蒙特卡洛法，该方法利用生成模型和人工神经网络。我们将此方法应用于弱耦合策略中的标量$φ^4 $晶格场理论，并在此过程中大大增加了迄今为止使用这种自学习技术探索的系统尺寸。我们的方法不依赖于先前存在的样本培训集，因为代理通过模型本身收集的引导样本系统地改善了其性能。我们通过检查训练有素的模型的混合时间并研究了生成的样品的刻薄性，从而评估了训练有素的模型的性能。与诸如汉密尔顿蒙特卡洛之类的方法相比，这种方法提供了独特的优势，例如推理速度和蒙特卡洛提议的压缩表示，以便在下游任务中使用。
We explore a self-learning Markov chain Monte Carlo method based on the Adversarial Non-linear Independent Components Estimation Monte Carlo, which utilizes generative models and artificial neural networks. We apply this method to the scalar $φ^4$ lattice field theory in the weak-coupling regime and, in doing so, greatly increase the system sizes explored to date with this self-learning technique. Our approach does not rely on a pre-existing training set of samples, as the agent systematically improves its performance by bootstrapping samples collected by the model itself. We evaluate the performance of the trained model by examining its mixing time and study the ergodicity of generated samples. When compared to methods such as Hamiltonian Monte Carlo, this approach provides unique advantages such as the speed of inference and a compressed representation of Monte Carlo proposals for potential use in downstream tasks.
