arXiv subjects

Tingting Xue

Publications and source records attributed to Tingting Xue.

5 recordsLinked to original sources

arXiv2026

State-resolved multimodal contributions to stratospheric polar vortex predictability

The dynamical basis of stratospheric polar vortex predictability remains unclear, particularly the relative roles of persistence, structural variability, and cross-level coupling. Here we provide a state-resolved and quantitative framework using eigen microstate theory applied to ERA5 geopotential height fields, enabling attribution of predictability to dynamically coherent circulation states via a mesoscopic Granger-causality approach. We show that short-term predictability is dominated by persistence of the leading stratospheric state, whereas extended predictability arises from higher-order stratospheric structures and tropospheric variability. These contributions exhibit strong lead-time dependence and become more distributed during sudden stratospheric warming events. Our results unify SPV predictability within a multimodal, state-resolved framework and provide a physically interpretable pathway for improving subseasonal-to-seasonal forecasts.

physics.ao-ph↗

arXiv2026

Machine-learning extraction of size-dependent temperature scales in the 2D XY model

Machine learning has become a useful tool for studying phase transitions in statistical systems.For the two-dimensional classical XY model, however, the topological character of the Berezinskii-Kosterlitz-Thouless (BKT) transition and pronounced finite-size effects make it nontrivial to extract robust size-dependent pseudo-critical temperatures from configuration data. Existing studies often stop at phase classification, leaving open how standard neural-network outputs can be turned into quantitatively testable observables. Here we develop a machine learning-assisted framework for the 2D XY model that uses standard network outputs to extract the size-dependent sequence of pseudo-critical temperatures T(L). Specifically, we generate Monte Carlo configurations using embedded cluster updates, train a standard ResNet18 only on samples from the Quasi-ordered Phase and the Disordered Phase, and determine T(L) from bootstrap-averaged probability curves using the 50% crossing criterion. We then analyze the finite size drift of this temperature sequence using BKT-motivated scaling and compare it with susceptibility-peak temperatures. The resulting temperature sequence shows a systematic finitesize drift consistent with BKT-type behavior and remains in the same fluctuation window as the susceptibility peak, supporting its interpretation as a finite-size pseudo-critical temperature. More broadly, this framework provides a practical route for converting standard neural-network outputs into physically interpretable finite-size observables in systems with strong crossover or topological transition signatures

physics.soc-ph↗

arXiv2022

Machine learning phases of active matter

Recent years have witnessed a growing interest in using machine learning to predict and identify phase transitions in various systems. Here we adopt convolutional neural networks (CNNs) to study the phase transitions of Vicsek model, solving the problem that traditional order parameters are insufficiently able to do. Within the large-scale simulations, there are four phases, and we confirm that all the phase transitions between two neighboring phases are first-order. We have successfully classified the phase by using CNNs with a high accuracy and identified the phase transition points, while traditional approaches using various order parameters fail to obtain. These results indicate that the great potential of machine learning approach in understanding the complexities in collective behaviors, and in related complex systems in general.

physics.bio-ph↗

arXiv2020

Swarming transitions in hierarchical societies

Social hierarchy is central to decision-making in the coordinated movement of many swarming species. Here we propose a hierarchical swarm model in the spirit of the Vicsek model of self-propelled particles. We show that, as the hierarchy becomes important, the swarming transition changes from the weak first-order transition observed for egalitarian populations, to a stronger first-order transition for intermediately strong hierarchies, and finally the discontinuity reduces till vanish, where the order-disorder transition appears to be absent in the extremely despotic societies. Associated to this we observe that the spatial structure of the swarm, as measured by the correlation between the density and velocity fields, is strongly mediated by the hierarchy. A two-group model and vectorial noise are also studied for verification. Our results point out the particular relevance of the hierarchical structures to swarming transitions when doing specific case studies.

physics.bio-ph↗

arXiv2018

Resonant synchronization and information retrieve from memorized Kuramoto network

A new collective behavior of resonant synchronization is discovered and the ability to retrieve information from brain memory is proposed based on this mechanism. We use modified Kuramoto phase oscillator to simulate the dynamics of a single neuron in self-oscillation state, and investigate the collective responses of a neural network, which is composed of $N$ globally coupled Kuramoto oscillators, to the external stimulus signals in a critical state just below the synchronization threshold of Kuramoto model. The input signals at different driving frequencies, which are used to denote different neural stimuli, can drive the coupled oscillators into different synchronized groups locked to the same effective frequencies and recover different synchronized patterns emerged from their collective dynamics closely related to the predetermined frequency distributions of the oscillators (memory). This model is used to explain how brain stores and retrieves information by the synchronized patterns emerging in the neural network stimulated by the external inputs.

nlin.AO↗