Italiano(IT) Dreaming up scale invariance via inverse renormalization group

AI模型学会反转重整化群以进行物理模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员开发了最小的神经网络，能够反转二维Ising模型中的重整化群粗粒化过程。这些网络能够概率性地重建尺度不变分布，并以少至三个可训练参数生成临界构型。模型成功地重现了可观测量值的标度行为，并捕获了重整化群变换的非平凡特征值，这表明简单的局部规则可以编码临界现象的普适性。 AI

影响展示了简单生成模型捕获复杂物理现象的潜力，可能影响未来用于科学发现的AI架构。

排序理由这是一篇关于arXiv的arXiv研究论文，详细介绍了一种在统计物理学中生成模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 Italiano(IT) · Adam Ran\c{c}on, Ulysse Ran\c{c}on, Tomislav Ivek, Ivan Balog · 2026-05-08 04:00

通过反向重整化群构想尺度不变性

arXiv:2506.04016v2 Announce Type: replace-cross Abstract: We explore how minimal neural networks can invert the renormalization group (RG) coarse-graining procedure in the two-dimensional Ising model, effectively `"dreaming up'' microscopic configurations from coarse-grained stat…