arXiv subjects

Pei-Fang Wu

Publications and source records attributed to Pei-Fang Wu.

2 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↗

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↗