AI 中文总结
该研究提出一种重正化群引导的机器学习算法，通过完美阻塞、条件归一化流和再热化，实现从L≤32递归级联至L=2048的二维φ⁴临界格点场生成，保留正确长距离物理特性。
AI 中文摘要
我们提出一种基于重正化群（RG）变换近似逆的、用于格点场生成的RG引导机器学习算法。“完美阻塞”结构提供平衡的长距离模式，条件归一化流重构短距离细节，简要再热化消除残差误差。在临界二维φ⁴理论中，在L≤32处训练的流被递归复用至L=2048的级联，具有正确的长距离物理特性。
英文摘要
We introduce a renormalization-group (RG) guided machine-learning algorithm for lattice field generation based on approximate inversion of an RG transformation. A ``perfect blocking'' construction supplies equilibrated long-distance modes, while a conditional normalizing flow reconstructs short-distance details and brief rethermalization removes residual errors. In 2D $ϕ^4$ theory at criticality, a flow trained at $L\le32$ is reused recursively in cascades reaching $L=2048$ with correct long-distance physics.
Comments7 pages, 4 figures. Submitted to Phys. Rev. Lett
