标题： 基于深度学习的全息术用于T线性电阻率 显示英文标题

标题： Deep learning-based holography for T-linear resistivity

摘要： 我们利用全息对偶中的深度学习来研究$T$-线性电阻率，这是奇异金属的特征。 利用物理信息神经网络，我们将$T$-线性电阻率的边界数据和体微分方程纳入损失函数。 这种方法使我们能够推导出爱因斯坦-麦克斯韦-标量-轴子理论中的标量势，捕捉奇异金属的关键特性，如$T$-线性电阻率和线性比热尺度。 我们还探讨了电阻率斜率对标量势的影响。 无论斜率如何，标量势在低温下表现出普遍的指数增长，驱动$T$-线性电阻率，并与红外几何分析相匹配。 在特定斜率下，我们的方法重新发现了 Gubser-Rocha 模型，这是一个著名的奇异金属全息模型。 此外，高温下的$T$-线性电阻率的鲁棒性与标量耦合到麦克斯韦项的渐近 AdS 行为相关。 我们的研究结果表明，深度学习可能有助于揭示全息凝聚态系统中的机制，并推进我们对奇异金属的理解。 显示英文摘要

摘要： We employ deep learning within holographic duality to investigate $T$-linear resistivity, a hallmark of strange metals. Utilizing Physics-Informed Neural Networks, we incorporate boundary data for $T$-linear resistivity and bulk differential equations into a loss function. This approach allows us to derive dilaton potentials in Einstein-Maxwell-Dilaton-Axion theories, capturing essential features of strange metals, such as $T$-linear resistivity and linear specific heat scaling. We also explore the impact of the resistivity slope on dilaton potentials. Regardless of slope, dilaton potentials exhibit universal exponential growth at low temperatures, driving $T$-linear resistivity and matching infrared geometric analyses. At a specific slope, our method rediscovers the Gubser-Rocha model, a well-known holographic model of strange metals. Additionally, the robustness of $T$-linear resistivity at higher temperatures correlates with the asymptotic AdS behavior of the dilaton coupling to the Maxwell term. Our findings suggest that deep learning could help uncover mechanisms in holographic condensed matter systems and advance our understanding of strange metals.