arXiv subjects

Wei-Chen Guo

Publications and source records attributed to Wei-Chen Guo.

5 recordsLinked to original sources

arXiv2026

Emergent Macroscopic Nonreciprocity from Identical Active Particles via Spontaneous Symmetry Breaking

Nonreciprocity is known to generate a wide range of exotic phenomena in multi-species many-body systems, where different species influence one another through couplings that violate Newton's third law. In contrast, in the absence of explicitly imposed macroscopic nonreciprocal processes, single-species nonreciprocity -- another distinct form of nonreciprocity -- typically plays only a limited role in shaping macroscopic physics. Here, using a single-species Vicsek model with a vision cone and extrinsic noise, we show that spontaneous symmetry breaking (SSB) can dramatically enhance the macroscopic consequences of microscopic single-species nonreciprocity. In the ordered phase, this enhancement gives rise to an emergent macroscopic nonreciprocity that induces the system of identical active particles to admit an effective description with a "two-species" non-Hermitian structure. The resulting SSB-enhanced nonreciprocity substantially promotes traveling-band formation and, more strikingly, drives a novel real-space condensation of identical active particles, characterized by a "traveling line" with vanishing longitudinal width. Our findings uncover a fundamental mechanism by which microscopic single-species nonreciprocity can exert strong macroscopic influences in complex systems.

cond-mat.stat-mech↗

arXiv2025

Barrier induced stalemate-consensus transition of self-propelled participants subject to majority rule

Natural or artificial barriers, such as the Himalayas, the Berlin Wall, or the Korean Demilitarized Zone, can significantly impede human migration. As a consequence, they may also hinder the dissemination of opinions within society, thereby contributing to divergent geopolitical landscapes and cultural developments. This raises a fundamental question: how do such barriers influence the opinion dynamics of mobile agents, such as human beings? In particular, can a barrier induce transitions in collective opinion states among spatially segregated groups? Here, we investigate the opinion dynamics governed by majority rule in a minimal model comprising self-propelled agents with binary opinions performing random walks within a closed space divided by a barrier. We focus on the conditions under which initially segregated clusters of agents with opposing opinions can reach consensus. Our results reveal the existence of a critical barrier size that marks a transition between stalemate and consensus states. Near this critical point, the relaxation time to reach consensus from an initial stalemate exhibits a power-law divergence. This barrier-induced stalemate-consensus transition in a simple agent-based model offers new insights into the role of physical or social barriers in shaping opinion dynamics and social structures.

physics.soc-ph↗

arXiv2024

Noise-aware neural network for stochastic dynamics simulation

In the presence of system-environment coupling, classical complex systems undergo stochastic dynamics, where rich phenomena can emerge at large spatio-temporal scales. To investigate these phenomena, numerical approaches for simulating stochastic dynamics are indispensable and can be computationally expensive. In light of the recent fast development in machine learning techniques, here, we establish a generic machine learning approach to simulate the stochastic dynamics, dubbed the noise-aware neural network (NANN). One key feature of this approach is its ability to generate the long-time stochastic dynamics of complex large-scale systems by just training NANN with the one-step dynamics of smaller-scale systems, thus reducing the computational cost. Furthermore, this NANN based approach is quite generic. Case-by-case special design of the architecture of NANN is not necessary when it is employed to investigate different stochastic complex systems. Using the noisy Kuramoto model and the Vicsek model as concrete examples, we demonstrate its capability in simulating stochastic dynamics. We believe that this novel machine learning approach can be a useful tool in investigating the large spatio-temporal scaling behavior of complex systems subjected to the influences of the environmental noise.

cond-mat.stat-mech↗

arXiv2023

Learning phase transitions from regression uncertainty: A new regression-based machine learning approach for automated detection of phases of matter

For performing regression tasks involved in various physics problems, enhancing the precision or equivalently reducing the uncertainty of regression results is undoubtedly one of the central goals. Here, somewhat surprisingly, we find that the unfavorable regression uncertainty in performing the regression tasks of inverse statistical problems actually contains hidden information concerning the phase transitions of the system under consideration. By utilizing this hidden information, we develop a new unsupervised machine learning approach for automated detection of phases of matter, dubbed learning from regression uncertainty. This is achieved by revealing an intrinsic connection between regression uncertainty and response properties of the system, thus making the outputs of this machine learning approach directly interpretable via conventional notions of physics. We demonstrate the approach by identifying the critical points of the ferromagnetic Ising model and the three-state clock model, and revealing the existence of the intermediate phase in the six-state and seven-state clock models. Comparing to the widely-used classification-based approaches developed so far, although successful, their recognized classes of patterns are essentially abstract, which hinders their straightforward relation to conventional notions of physics. These challenges persist even when one employs the state-of-the-art deep neural networks that excel at classification tasks. In contrast, with the core working horse being a neural network performing regression tasks, our new approach is not only practically more efficient, but also paves the way towards intriguing possibilities for unveiling new physics via machine learning in a physically interpretable manner.

cond-mat.stat-mech↗

arXiv2023

Reveal flocking phase transition of self-propelled active particles by machine learning regression uncertainty

We develop the neural network based "learning from regression uncertainty" approach for automated detection of phases of matter in nonequilibrium active systems. Taking the flocking phase transition of self-propelled active particles described by the Vicsek model for example, we find that after training a neural network for solving the inverse statistical problem, i.e., for performing the regression task of reconstructing the noise level from given samples of such a nonequilibrium many-body complex system's steady state configurations, the uncertainty of regression results obtained by the well-trained network can actually be utilized to reveal possible phase transitions in the system under study. The noise level dependence of regression uncertainty assumes a non-trivial M-shape, and its valley appears at the critical point of the flocking phase transition. By directly comparing this regression-based approach with the widely-used classification-based "learning by confusion" and "learning with blanking" approaches, we show that our approach has practical effectiveness, efficiency, good generality for various physical systems across interdisciplinary fields, and a greater possibility of being interpretable via conventional notions of physics. These approaches can complement each other to serve as a promising generic toolbox for investigating rich critical phenomena and providing data-driven evidence on the existence of various phase transitions, especially for those complex scenarios associated with first-order phase transitions or nonequilibrium active systems where traditional research methods in physics could face difficulties.

cond-mat.dis-nn↗