Active Matter Flocking via Predictive Alignment
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915363450519552 |
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| author | Giraldo-Barreto, Julian Holubec, Viktor |
| author_facet | Giraldo-Barreto, Julian Holubec, Viktor |
| contents | Understanding collective self-organization in active matter, such as bird flocks and fish schools, remains a grand challenge in physics. Interactions that induce alignment are essential for flocking; however, alignment alone is generally insufficient to maintain group cohesion in the presence of noise, leading traditional models to introduce artificial boundaries or explicit attractive forces. Here, we propose a model that achieves cohesive flocking through purely alignment-based interactions by introducing predictive alignment, in which agents reorient to maximize alignment with the prevailing orientations of their anticipated future neighbors. Implemented in a discrete-time Vicsek-type framework, this approach delivers robust, noise-resistant cohesion without additional parameters. In the stable regime, flock size scales linearly with interaction radius, remaining nearly immune to noise or propulsion speed, and the group coherently follows a leader under noise. These findings reveal how predictive strategies enhance self-organization, paving the way for a new class of active matter models blending physics and cognitive-like dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Active Matter Flocking via Predictive Alignment Giraldo-Barreto, Julian Holubec, Viktor Soft Condensed Matter Adaptation and Self-Organizing Systems Understanding collective self-organization in active matter, such as bird flocks and fish schools, remains a grand challenge in physics. Interactions that induce alignment are essential for flocking; however, alignment alone is generally insufficient to maintain group cohesion in the presence of noise, leading traditional models to introduce artificial boundaries or explicit attractive forces. Here, we propose a model that achieves cohesive flocking through purely alignment-based interactions by introducing predictive alignment, in which agents reorient to maximize alignment with the prevailing orientations of their anticipated future neighbors. Implemented in a discrete-time Vicsek-type framework, this approach delivers robust, noise-resistant cohesion without additional parameters. In the stable regime, flock size scales linearly with interaction radius, remaining nearly immune to noise or propulsion speed, and the group coherently follows a leader under noise. These findings reveal how predictive strategies enhance self-organization, paving the way for a new class of active matter models blending physics and cognitive-like dynamics. |
| title | Active Matter Flocking via Predictive Alignment |
| topic | Soft Condensed Matter Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2504.07778 |