arXiv subjects

Yu-Kun Yan

Publications and source records attributed to Yu-Kun Yan.

7 recordsLinked to original sources

arXiv2026

Splitting Dynamics of Multiply Quantized Vortices in Holographic Superfluid of Finite Temperature

We study the splitting dynamics of multiply quantized vortices with winding numbers $n=5,6,7$ and $8$ in a two-dimensional holographic superfluid at finite temperature, by combining linear perturbation analysis of quasinormal modes with fully nonlinear real-time numerical simulations. Three new physical phenomena are revealed. First, the number of unstable modes no longer strictly follows the $2n-3$ formula as $n$ increases. For the vortex with $n=8$, the unstable mode with $p=2(n-1)$ is absent throughout the entire temperature range, so that only $2n-4$ unstable modes exist. Second, the transition of the dominant unstable mode with increasing temperature exhibits new characteristics. For vortices with $n\le 6$, the dominant mode changes sequentially as $p=2,3,\dots,n$, whereas for $n\ge 7$ jump-like transitions occur-for instance, for $n=7$ the dominant mode jumps from $p=2$ to $p=4$ at $T=0.325T_c$ and then directly to $p=7$ at $T=0.359T_c$, and for $n=8$ it jumps directly from $p=2$ to $p=8$ at $T=0.302T_c$. Third, a single splitting pattern of high-winding-number vortices can contain multiple sub-splitting patterns with distinct topological structures, as exemplified by the $l=4$ pattern of the $n=8$ vortex, which exhibits three sub-patterns at low, intermediate and high temperatures. The nonlinear simulations confirm the predictions of the linear stability analysis, and the implications of our results for cold-atom experiments are discussed.

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arXiv2026

Transition of vortex dipole dynamics in holographic superfluids

Using holographic duality, we reveal a transition in vortex dipole dynamics below a critical dipole size in strongly interacting superfluids, characterized by a significant suppression of mutual friction. In the bulk, this transition is triggered by a topological reconnection of vortex tubes, which disconnects the boundary vortices from the black hole horizon and forms a \textit{U-pipe}. Consequently, the post-transition evolution is governed by the contraction of the bulk \textit{U-pipe} rather than the mutual friction associated with the horizon, revealing a scale-dependent dissipation mechanism. We further show that this reconnection persists over a broad temperature range, even when the transition becomes unobservable at high temperatures. Our results provide a dissipation-based interpretation for the anomalous critical dipole scale observed in strongly interacting cold-atom experiments, and suggest the existence of distinct dissipative regimes in strongly interacting superfluids.

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arXiv2024

Vortex shedding patterns in holographic superfluids at finite temperature

The dynamics of superfluid systems exhibit significant similarities to their classical counterparts, particularly in the phenomenon of vortex shedding triggered by a moving obstacle. In such systems, the universal behavior of shedding patterns can be classified using the classical concept of the Reynolds number $Re=\frac{v \sigma}{\nu}$ (characteristic length scale $\sigma$, velocity $v$ and viscosity $\nu$), which has been shown to generalize to quantum systems at absolute zero temperature. However, it remains unclear whether this universal behavior holds at finite temperatures, where viscosity arises from two distinct sources: thermal excitations and quantum vortex viscosity. Using a holographic model of finite-temperature superfluids, we investigate the vortex shedding patterns and identify two distinct regimes without quantum counterparts: a periodic vortex dipole pattern and a vortex dipole train pattern. By calculating the shedding frequency, Reynolds number, and Strouhal number, we find that these behaviors are qualitatively similar to empirical observations in both classical and quantum counterparts, which imply the robustness of vortex shedding dynamics at finite-temperature superfluid systems.

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arXiv2024

Neural ODEs for holographic transport models without translation symmetry

We investigate the data-driven holographic transport models without translation symmetry, focusing on the real part of frequency-dependent shear viscosity, $\eta_{\mathrm{re}}(\omega)$. We develop a radial flow equation of the shear response and establish its relation to $\eta _{\mathrm{re}}(\omega)$ for a wide class of holographic models. This allows us to determine $\eta _{\mathrm{re}}(\omega )$ of a strongly coupled field theory by the black hole metric and the graviton mass. The latter serves as the bulk dual to the translation symmetry breaking on the boundary. We convert the flow equation to a Neural Ordinary Differential Equation (Neural ODE), which is a neural network with continuous depth and produces output through a black-box ODE solver. Testing the Neural ODE on three well-known holographic models without translation symmetry, we demonstrate its ability to accurately learn either the metric or mass when given the other. Additionally, we illustrate that the learned metric can be used to predict the derivative of entanglement entropy $S$ with respect to the size of entangling region $l$.

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arXiv2023

Holographic dissipation prefers the Landau over the Keldysh form

Although holographic duality has been regarded as a complementary tool in helping understand the non-equilibrium dynamics of strongly coupled many-body systems, it still remains a remarkable challenge how to confront its predictions quantitatively with the real experimental scenarios. By matching the holographic vortex dynamics with the phenomenological dissipative Gross-Pitaeviskii models, we find that the holographic dissipation mechanism can be well captured by the Landau form rather than the Keldysh one, although the latter is much more widely used in numerical simulations. Our finding is expected to open up novel avenues for facilitating the quantitative test of the holographic predictions against the upcoming experimental data. Our result also provides a prime example how holographic duality can help select proper phenomenological models to describe far-from-equilibrium nonlinear dynamics beyond the hydrodynamic regime.

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arXiv2023

Splitting of doubly quantized vortices in holographic superfluid of finite temperature

The temperature effect on the linear instability and the splitting process of a doubly quantized vortex is studied. Using the linear perturbation theory to calculate out the quasi-normal modes of the doubly quantized vortex, we find that the imaginary part of the unstable mode increases with the temperature till some turning temperature, after which the imaginary part of the unstable mode decreases with the temperature. On the other hand, by the fully non-linear numerical simulations, we also examine the real time splitting process of the doubly quantized vortex, where not only do the split singly quantized vortex pair depart from each other, but also revolve around each other. In particular, the characteristic time scale for the splitting process is identified and its temperature dependence is found to be in good agreement with the linear instability analysis in the sense that the larger the imaginary part of the unstable mode is, the longer the splitting time is. Such a temperature effect is expected to be verified in the cold atom experiments in the near future.

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arXiv2020

Deep learning black hole metrics from shear viscosity

Based on AdS/CFT correspondence, we build a deep neural network to learn black hole metrics from the complex frequency-dependent shear viscosity. The network architecture provides a discretized representation of the holographic renormalization group flow of the shear viscosity and can be applied to a large class of strongly coupled field theories. Given the existence of the horizon and guided by the smoothness of spacetime, we show that Schwarzschild and Reissner-Nordström metrics can be learned accurately. Moreover, we illustrate that the generalization ability of the deep neural network can be excellent, which indicates that by using the black hole spacetime as a hidden data structure, a wide spectrum of the shear viscosity can be generated from a narrow frequency range. These results are further generalized to an Einstein-Maxwell-dilaton black hole. Our work might not only suggest a data-driven way to study holographic transports but also shed some light on holographic duality and deep learning.

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