English(EN) Spinning Conformal Correlators from Neural Networks

神经网络构建自旋共形场，恢复麦克斯韦共形场论

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-18 04:00

研究人员开发了一种使用神经网络和嵌入形式主义来构建自旋共形场的方法。该方法允许计算二点、三点和四点函数，并在此基础上扩展了现有的标量共形场技术。在具有独立同分布神经元的特定场景下，该方法在无限宽度极限下成功地重现了四维麦克斯韦共形场论。 AI

影响引入了神经网络在理论物理学中的一项新应用，有望推动共形场论和高能物理学研究。

排序理由该条目是arXiv上的一篇学术论文，详细介绍了一种使用神经网络构建自旋共形场的新方法。[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 English(EN) · Manas Dogra, James Halverson, Joydeep Naskar · 2026-08-18 04:00

神经网络生成共形相关器

arXiv:2608.15001v1 Announce Type: cross Abstract: We construct spinning conformal fields from neural networks and the embedding formalism, computing their two-, three- and four-point functions in examples, building on scalar conformal field techniques introduced in \cite{Halverso…