论文标题
虚无的差分进化算法，用于想象时间相关函数的分析延续
A Parameter-Free Differential Evolution Algorithm for the Analytic Continuation of Imaginary Time Correlation Functions
论文作者
论文摘要
我们报告了分析延续的差异进化（DEAC）：一种无参数的进化算法，可从假想时间相关函数中生成动态结构因子。我们在量子多体物理学中解决这个长期存在的问题的方法可以增强光谱保真度，同时使用较少的计算（CPU）小时。通过将它们嵌入基因组中以对基于进化计算的算法进行优化，从而消除了算法控制参数的微调需求。为模型提供了基准，该模型是在该模型中恰好知道的，并且在超出超流体过渡温度以下的大量$^4 $ HE的量子蒙特卡洛模拟中包括了与实验相关的结果。
We report on Differential Evolution for Analytic Continuation (DEAC): a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation based algorithm. Benchmarks are presented for models where the dynamic structure factor is known exactly, and experimentally relevant results are included for quantum Monte Carlo simulations of bulk $^4$He below the superfluid transition temperature.
