发表机构
North Carolina State University; Univ Toulouse, CNRS, Laboratoire de Physique Théorique（北卡罗来纳州立大学; 图卢兹大学，法国国家科学研究中心，理论物理实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MOR+EC 框架结合本征向量延续与模型降阶，构建参数无关预解式的降阶模型，高效计算强关联格林函数，以约10^-4误差和16-91倍加速实现高分辨率相图与光谱。
AI 中文摘要
在参数空间中重复评估单粒子格林函数（GF）是多体方法中反复出现的瓶颈，限制了相边界可探测的分辨率以及计算光谱的频率分辨率。我们引入了 MOR+EC，这是一个构建可重用降阶模型来计算单粒子格林函数的框架，它结合了用于参数空间探索的本征向量延续（EC）和用于频率空间外推的模型降阶（MOR）。与传统的参数化 MOR 不同，我们使用与参数无关的预解式构建这些降阶模型，进一步减少了所需的满空间评估次数。以精确对角化作为杂质求解器，对我们的单带和双带模型的 DMFT 计算示例进行基准测试，MOR+EC 将 DMFT 收敛的杂质格林函数再现到平均相对误差约为 $10^{-4}$，中位墙钟时间加速比为 $16-91\ imes$。这种精度和效率共同解析了随着掺杂调谐的杂质占据数的细粒度扫描，并产生了轨道选择性莫特转变的高分辨率相图。降阶模型还能以满空间评估的额外成本为零的方式再现实频和虚频光谱。该框架广泛适用于需要重复参数评估和高频率轴分辨率的基于格林函数的方法。
英文摘要
Repeated evaluation of the single-particle Green's function (GF) across parameter space is a recurring bottleneck in many-body methods, limiting the resolution at which phase boundaries may be probed and the frequency resolution of computed spectra. We introduce MOR+EC, a framework that constructs reusable reduced-order models for computing single-particle Green's functions by combining eigenvector continuation (EC) for parameter space exploration and model order reduction (MOR) for extrapolation in frequency space. Contrary to conventional parameterized MOR, we construct these reduced-order models with a parameter-independent resolvent, further reducing the number of required full-space evaluations. Benchmarking against exact diagonalization as the impurity solver for our example case of DMFT calculations for single- and two-band models, MOR+EC reproduces the DMFT-converged impurity GF to an average relative error of $\sim10^{-4}$, with a median wall-time speedup of $16-91\times$. This accuracy and efficiency together resolve a fine-grained scan of the impurity occupation as doping is tuned and produce a high-resolution phase diagram of an orbital-selective Mott transition. The reduced-order model also reproduces real- and imaginary-frequency spectra at no additional cost in full-space evaluations. This framework applies broadly to GF-based methods requiring repeated parametric evaluation and high resolution of the frequency axis.
Comments12 + 5 pages, 12 figures
